{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/nsanghi/drl-2ed/blob/main/chapter6/6.c-dqn_atari_pytorch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QtcwANgYGyLa"
   },
   "source": [
    "# DQN - Deep Q-Network  (PyTorch) - Play Atari game.\n",
    "\n",
    "In this notebook, we will use DQN with **experience replay** and **target networks** from `6.a-dqn-pytorch` to train the agent to play Atari game. Most of the implementation will be same as previous one, except some pre-processing to make Atari game playing trainable with DQN.\n",
    "\n",
    "The deep learning approach combined with Q-Learning and word `DQN` was coined in this paper. We have tried to broadly follow the steps of the paper in this notebook\n",
    "\n",
    "Paper: [Playing Atari with Deep Reinforcement Learning](https://arxiv.org/pdf/1312.5602.pdf)\n",
    "\n",
    "### Recap from last notebook\n",
    "\n",
    "Q Learning control is carried out by sampling step by step and updating Q values at each step. We use $\\epsilon$-greedy policy to explore and generate samples. However, the policy learnt is a deterministic greedy policy with no exploration. We can carryout updates online i.e. we take a step and use `(current state, action, reward and next_state)` tuple to update.\n",
    "\n",
    "In case of function approximation using neural network, the input to the network is the state and output is the $q(s,a)$ for all the actions in the state $s$. It is denoted as $ \\hat{q}(s_t, a_t; w_{t}) $, where $w_{t}$ is the weigths of the neural network that we learn as part of DQN learning.\n",
    "\n",
    "We use two networks, one target network with weight $w^-_t$ to get the max $q$-value of next state with best action denoted by $ \\max\\limits_a \\hat {q}(S_{t+1},a; w^{-}_{t}) $ and network with weights $w_t^-$ which we periodically updated from primary network $w_t$.\n",
    "\n",
    "The Update equation is given below. This is the online version:\n",
    "$$ w_{t+1} \\leftarrow w_t + \\alpha [ R_{t+1} + \\gamma . \\max_{a} \\hat{q}(S_{t+1},a;w^{-}_{t}) – \\hat{q}(S_t,A_t;w_t)] \\nabla \\hat{q}_{w_t}(S_t,A_t;w_t)$$\n",
    "\n",
    "Online update with neural network with millions of weights does not work well. Accordingly, We use experience replay (aka Replay Buffer).  We use a behavior policy to explore the environment and store the samples `(s, a, r, s', done)` in a buffer. The samples are generated using an exploratory behavior policy while we improve a deterministic target policy using q-values.\n",
    "\n",
    "Therefore, we can always use older samples from behavior policy and apply them again and again. We can keep the buffer size fixed to some pre-determined size and keep deleting the older samples as we collect new ones. This process makes learning sample efficient by reusing a sample multiple times and also removing temporal dependence of the samples we would otherwise see while following a trajectory.\n",
    "\n",
    "The update equation with batch update with minor modifications is given below. We collect samples of transitions `(current state, action, reward, next state)` in a buffer, where each sample is denoted as a tuple:\n",
    "\n",
    "$$ (s_{i}, a_{i}, r_{i}, s'_{i}, done_{i})$$\n",
    "\n",
    "Subscript ($i$) denotes ith sample. We take $N$ samples from experience replay selecting randomly and update the weights. Subscript ($t$) denotes the index of weight updates. If the current state is done, as denoted by `done` flag, the target is just the reward as terminal states have zero value. The final update equation is as given below:\n",
    "\n",
    "$$w_{t+1} \\leftarrow w_t + \\alpha \\frac{1}{N} \\sum_{i=1}^{N} \\left[ r_i + \\left( (1-done_i) . \\gamma .  \\max_{a'} \\hat{q}(s'_{i},a';w^{-}_{t}) \\right) – \\hat{q}(s_i,a_i;w_t) \\right] \\nabla \\hat{q}(s_i,a_i;w_t)$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f8HytoN9GyLd"
   },
   "source": [
    "#### Running in Colab/Kaggle\n",
    "\n",
    "If you are running this on Colab, please uncomment below cells and run this to install required dependencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## uncomment and execute this cell to install all the the dependencies if running in Google Colab or Kaggle\n",
    "# !apt-get update \n",
    "# !apt-get install -y swig cmake ffmpeg freeglut3-dev xvfb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Uncomment and execute this cell to install all the the dependencies if running in Google Colab or Kaggle\n",
    "\n",
    "## Uncomment and run for Colab\n",
    "# !git clone https://github.com/nsanghi/drl-2ed\n",
    "# %cd /content/drl-2ed \n",
    "# !pip install  -r requirements.txt\n",
    "# %cd chapter6\n",
    "\n",
    "\n",
    "## Uncomment and run for Kaggle\n",
    "# !git clone https://github.com/nsanghi/drl-2ed\n",
    "# %cd /kaggle/working/drl-2ed \n",
    "# !pip install  -r requirements.txt\n",
    "# %cd chapter6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Uncomment and Rerun the cd command in case you were asked to restart the kernel and you followed that message\n",
    "## as after resart the kernel will again point back to root folder\n",
    "\n",
    "\n",
    "## Uncomment and run for Colab\n",
    "# %cd /content/drl-2ed \n",
    "# %cd chapter6\n",
    "\n",
    "\n",
    "## Uncomment and run for Kaggle\n",
    "# %cd /kaggle/working/drl-2ed \n",
    "# %cd chapter6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "ykZ8l9kzGyLf"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-04-14 17:44:05.973192: I tensorflow/core/util/port.cc:111] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2024-04-14 17:44:05.989397: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-04-14 17:44:06.104225: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-04-14 17:44:06.104448: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-04-14 17:44:06.105120: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-04-14 17:44:06.162841: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-04-14 17:44:06.164234: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-04-14 17:44:06.892319: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import gymnasium as gym\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.signal import convolve, gaussian\n",
    "from stable_baselines3.common.vec_env import VecVideoRecorder, DummyVecEnv\n",
    "from base64 import b64encode\n",
    "\n",
    "from IPython.display import HTML\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ck-wiMZVGyLg"
   },
   "source": [
    "### Environment - Atari Breakout\n",
    "\n",
    "We can use the setup here to run on any environment which has state as a single vector and actions are discrete. We will build it on Atari Breakout.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "Nu-PQAFBGyLh"
   },
   "outputs": [],
   "source": [
    "def make_env(env_name, frameskip=5, repeat_action_probability=0.25, render_mode='rgb_array', mode=0, difficulty=0):\n",
    "    # remove time limit wrapper from environment\n",
    "    env = gym.make(env_name,\n",
    "                   render_mode=render_mode,\n",
    "                   frameskip=frameskip,\n",
    "                   repeat_action_probability=repeat_action_probability,\n",
    "                   mode=mode,\n",
    "                   difficulty=difficulty\n",
    "                  ).unwrapped\n",
    "    return env"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 695
    },
    "id": "d5CaXGXSGyLh",
    "outputId": "d8738915-69bb-496b-ff0c-24d1e8e31020"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "A.L.E: Arcade Learning Environment (version 0.8.1+53f58b7)\n",
      "[Powered by Stella]\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x900 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "env_name = \"BreakoutNoFrameskip-v4\"\n",
    "\n",
    "env = make_env(env_name)\n",
    "env.reset(seed=127)\n",
    "\n",
    "n_cols = 4\n",
    "n_rows = 2\n",
    "fig = plt.figure(figsize=(16, 9))\n",
    "\n",
    "for row in range(n_rows):\n",
    "    for col in range(n_cols):\n",
    "        ax = fig.add_subplot(n_rows, n_cols, row * n_cols + col + 1)\n",
    "        ax.imshow(env.render())\n",
    "        env.step(env.action_space.sample())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZFDidQRTGyLh"
   },
   "source": [
    "#### We can play the game\n",
    "\n",
    "use `A` and `D` on keyboard to move the bat and `Space-bar` to start. use `Esc` to kill the game.\n",
    "\n",
    "**NOTE: Running the cell below may crash your kernel in Mac. If you face that problem, please comment the cell below or do not run it**.\n",
    "\n",
    "**NOTE: Below cell will not run in Google Colab or Kaggle**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "mEH7bN5FGyLh"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nsanghi/sandbox/apress/drl-2ed/venv39/lib/python3.9/site-packages/gymnasium/utils/play.py:29: UserWarning: \u001b[33mWARN: matplotlib is not installed, run `pip install gymnasium[other]`\u001b[0m\n",
      "  logger.warn(\"matplotlib is not installed, run `pip install gymnasium[other]`\")\n",
      "/home/nsanghi/sandbox/apress/drl-2ed/venv39/lib/python3.9/site-packages/gymnasium/core.py:311: UserWarning: \u001b[33mWARN: env.get_keys_to_action to get variables from other wrappers is deprecated and will be removed in v1.0, to get this variable you can do `env.unwrapped.get_keys_to_action` for environment variables or `env.get_wrapper_attr('get_keys_to_action')` that will search the reminding wrappers.\u001b[0m\n",
      "  logger.warn(\n",
      "/home/nsanghi/sandbox/apress/drl-2ed/venv39/lib/python3.9/site-packages/gymnasium/utils/passive_env_checker.py:335: UserWarning: \u001b[33mWARN: No render fps was declared in the environment (env.metadata['render_fps'] is None or not defined), rendering may occur at inconsistent fps.\u001b[0m\n",
      "  logger.warn(\n"
     ]
    }
   ],
   "source": [
    "from gymnasium.utils.play import play\n",
    "\n",
    "play(env=gym.make(env_name,\n",
    "                  render_mode='rgb_array',\n",
    "                  frameskip=1,\n",
    "                  repeat_action_probability=0.0,\n",
    "                  mode=0,\n",
    "                  difficulty=0\n",
    "                 ), zoom=3.5, fps=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J7oVndrAGyLi"
   },
   "source": [
    "### Pre processing\n",
    "\n",
    "Atari images are 210x160x3 pixels but we will scale it down and also convert to gray-scale to reduce the size and make the game learn faster from smaller image sizes. We will also stack 4 last frames as input observation. This is done to capture the motion of the ball and bat which would not be visible in a single frame of game. Gymnasium library has ObservationWrappers to provide all these pre-processing out of the box. We will also be clipping rewards with just the sign of rewards. This is following the approach outlined in original paper. Let us build our preprocessor below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "BcVkOSzTGyLi"
   },
   "outputs": [],
   "source": [
    "from gymnasium.wrappers import AtariPreprocessing\n",
    "from gymnasium.wrappers import FrameStack\n",
    "from gymnasium.wrappers import TransformReward\n",
    "\n",
    "def make_env(env_name,\n",
    "             clip_rewards=True):\n",
    "\n",
    "    env = gym.make(env_name,\n",
    "                   render_mode='rgb_array',\n",
    "                   frameskip=1\n",
    "              )\n",
    "    env = AtariPreprocessing(env, screen_size=84, scale_obs=True)\n",
    "    env = FrameStack(env, num_stack=4)\n",
    "    if clip_rewards:\n",
    "        env = TransformReward(env, lambda r: np.sign(r))\n",
    "    return env\n",
    "\n",
    "env = make_env(env_name)\n",
    "obs, _ = env.reset()\n",
    "n_actions = env.action_space.n\n",
    "state_shape = env.observation_space.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iEjBK1zdGyLj",
    "outputId": "fbd5bb28-16ff-4585-b8d0-e41e600b0fdc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observation Shape: (4, 84, 84)\n",
      "Actions Array: ['NOOP', 'FIRE', 'RIGHT', 'LEFT']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nsanghi/sandbox/apress/drl-2ed/venv39/lib/python3.9/site-packages/gymnasium/core.py:311: UserWarning: \u001b[33mWARN: env.get_action_meanings to get variables from other wrappers is deprecated and will be removed in v1.0, to get this variable you can do `env.unwrapped.get_action_meanings` for environment variables or `env.get_wrapper_attr('get_action_meanings')` that will search the reminding wrappers.\u001b[0m\n",
      "  logger.warn(\n"
     ]
    }
   ],
   "source": [
    "print(\"Observation Shape:\", state_shape)\n",
    "print(\"Actions Array:\", env.get_action_meanings())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "cxQSoDgzGyLk",
    "outputId": "9994c8ac-d9fe-4bb9-bd2d-08c65320728d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for _ in range(12):\n",
    "#     obs, _, _, _, _ = env.step(env.action_space.sample())\n",
    "\n",
    "plt.figure(figsize=[12,10])\n",
    "plt.title(\"Game image\")\n",
    "plt.imshow(env.render())\n",
    "plt.show()\n",
    "\n",
    "obs = obs[:] #unpack lazyframe\n",
    "obs = np.transpose(obs,[1,0,2]) #move axes\n",
    "obs = obs.reshape((obs.shape[0], -1))\n",
    "\n",
    "\n",
    "plt.figure(figsize=[15,15])\n",
    "plt.title(\"Agent observation (4 frames left to right)\")\n",
    "plt.imshow(obs, cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DahYeTlpGyLk"
   },
   "source": [
    "### Building a network using pytorch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ujNRRRGhGyLk"
   },
   "source": [
    "Let us build a DQN agent using the architecture suggested in paper.\n",
    "\n",
    "    Observation:\n",
    "        pytorch 4x84x84 pytroch has channels in the first dimension\n",
    "        input: [batch_size, 4, 84, 84]\n",
    "        1st hidden layer: 16 8x8 filters with stride 4 and RelU activation\n",
    "        2nd hidden layer: 32 4x4 filters with stride of 2 and RelU activation\n",
    "        3nd hidden layer: Linear layer with 256 outputs and RelU activatrion\n",
    "        output layer: Linear with n_actions units with no activation\n",
    "\n",
    "To build this network, after 2 layers of convolution, we will flatten the layers. To connect this to Dense(Linear) layer, we need to calculate the number of units entering dense layer. We do so using the formula for Conv2D output.\n",
    "\n",
    "`out_dim = (in_dim - (kernel_size - 1) - 1) // stride  + 1`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "HpLwG3qeGyLk"
   },
   "outputs": [],
   "source": [
    "def conv2d_size_out(size, kernel_size, stride):\n",
    "    return (size - (kernel_size - 1) - 1) // stride  + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "poqiSRhOGyLk",
    "outputId": "2b8dd7ac-47bc-48a9-e302-b6e636e77885"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conv1:  20\n",
      "Conv1:  9\n",
      "Input to Dense layer: 2592\n"
     ]
    }
   ],
   "source": [
    "# for the network above\n",
    "# 1st Conv layer output size\n",
    "conv1 = conv2d_size_out(84, 8, 4)\n",
    "print('Conv1: ', conv1)\n",
    "conv2 = conv2d_size_out(conv1, 4, 2)\n",
    "print('Conv1: ', conv2)\n",
    "\n",
    "#number of units entering dense layer would be\n",
    "print(\"Input to Dense layer:\", conv2*conv2*32) #32 is number of filters coming out in 2nd conv layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xCYskbM5GyLl",
    "outputId": "6cf7c23e-67b8-4384-86d2-3c8ab1bb1adc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cpu')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "A6jKErHpGyLl"
   },
   "outputs": [],
   "source": [
    "class DQNAgent(nn.Module):\n",
    "    def __init__(self, state_shape, n_actions, epsilon=0):\n",
    "\n",
    "        super().__init__()\n",
    "        self.epsilon = epsilon\n",
    "        self.n_actions = n_actions\n",
    "        self.state_shape = state_shape\n",
    "\n",
    "        state_dim = state_shape[0]\n",
    "        # a simple NN with state_dim as input vector (inout is state s)\n",
    "        # and self.n_actions as output vector of logits of q(s, a)\n",
    "        self.network = nn.Sequential()\n",
    "        self.network.add_module('conv1', nn.Conv2d(4,16,kernel_size=8, stride=4))\n",
    "        self.network.add_module('relu1', nn.ReLU())\n",
    "        self.network.add_module('conv2', nn.Conv2d(16,32,kernel_size=4, stride=2))\n",
    "        self.network.add_module('relu2', nn.ReLU())\n",
    "        self.network.add_module('flatten', nn.Flatten())\n",
    "        self.network.add_module('linear3', nn.Linear(2592, 256)) #2592 calculated above\n",
    "        self.network.add_module('relu3', nn.ReLU())\n",
    "        self.network.add_module('linear4', nn.Linear(256, n_actions))\n",
    "\n",
    "        self.parameters = self.network.parameters\n",
    "\n",
    "    def forward(self, state_t):\n",
    "        # pass the state at time t through the newrok to get Q(s,a)\n",
    "        qvalues = self.network(state_t)\n",
    "        return qvalues\n",
    "\n",
    "    def get_qvalues(self, states):\n",
    "        # input is an array of states in numpy and outout is Qvals as numpy array\n",
    "        states = torch.tensor(states, device=device, dtype=torch.float32)\n",
    "        qvalues = self.forward(states)\n",
    "        return qvalues.data.cpu().numpy()\n",
    "\n",
    "    def sample_actions(self, qvalues):\n",
    "        # sample actions from a batch of q_values using epsilon greedy policy\n",
    "        epsilon = self.epsilon\n",
    "        batch_size, n_actions = qvalues.shape\n",
    "        random_actions = np.random.choice(n_actions, size=batch_size)\n",
    "        best_actions = qvalues.argmax(axis=-1)\n",
    "        should_explore = np.random.choice(\n",
    "            [0, 1], batch_size, p=[1-epsilon, epsilon])\n",
    "        return np.where(should_explore, random_actions, best_actions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "RKde9Zf1GyLl"
   },
   "outputs": [],
   "source": [
    "agent = DQNAgent(state_shape, n_actions, epsilon=0.5).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequential(\n",
      "  (conv1): Conv2d(4, 16, kernel_size=(8, 8), stride=(4, 4))\n",
      "  (relu1): ReLU()\n",
      "  (conv2): Conv2d(16, 32, kernel_size=(4, 4), stride=(2, 2))\n",
      "  (relu2): ReLU()\n",
      "  (flatten): Flatten(start_dim=1, end_dim=-1)\n",
      "  (linear3): Linear(in_features=2592, out_features=256, bias=True)\n",
      "  (relu3): ReLU()\n",
      "  (linear4): Linear(in_features=256, out_features=4, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(agent.network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "mLJbXd8dGyLl"
   },
   "outputs": [],
   "source": [
    "def evaluate(env, agent, n_games=1, greedy=False, t_max=10000):\n",
    "    rewards = []\n",
    "    for i in range(n_games):\n",
    "        s, _ = env.reset(seed=i)\n",
    "        reward = 0\n",
    "        for _ in range(t_max):\n",
    "            qvalues = agent.get_qvalues([s])\n",
    "            action = qvalues.argmax(axis=-1)[0] if greedy else agent.sample_actions(qvalues)[0]\n",
    "            s, r, terminated, truncated, _ = env.step(action)\n",
    "            reward += r\n",
    "            if terminated:\n",
    "                break\n",
    "\n",
    "        rewards.append(reward)\n",
    "    return np.mean(rewards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "axTTj8VJGyLl",
    "outputId": "24d4c138-f2d2-4f06-9d71-87bc7fef752f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_14152/693080632.py:31: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:261.)\n",
      "  states = torch.tensor(states, device=device, dtype=torch.float32)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate(env, agent, n_games=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "g-0zUtoAGyLl"
   },
   "outputs": [],
   "source": [
    "env.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Lt0Yk8Z7GyLm"
   },
   "source": [
    "### Experience replay\n",
    "\n",
    "We will use the replay buffer we saw in chapter 4 listings. Replay buffer is very important in DQN to break the correlation between samples. We use a behavior policy ($\\epsilon$-greedy) to sample from the environment and store the transitions (s,a,r,s',done) into a buffer. These samples are used multiple times in a learning making the process sample efficient.\n",
    "\n",
    "The interface to ReplayBuffer is:\n",
    "* `exp_replay.add(state, action, reward, next_state, done)` - saves (s,a,r,s',done) tuple into the buffer\n",
    "* `exp_replay.sample(batch_size)` - returns states, actions, rewards, next_states and done_flags for `batch_size` random samples.\n",
    "* `len(exp_replay)` - returns number of elements stored in replay buffer.\n",
    "\n",
    "We have modified the implementation a bit to make it more efficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "wC0sIbL5GyLm"
   },
   "outputs": [],
   "source": [
    "class ReplayBuffer:\n",
    "    def __init__(self, size):\n",
    "        self.size = size #max number of items in buffer\n",
    "        self.buffer =[] #array to hold buffer\n",
    "        self.next_id = 0\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.buffer)\n",
    "\n",
    "    def add(self, state, action, reward, next_state, done):\n",
    "        item = (state, action, reward, next_state, done)\n",
    "        if len(self.buffer) < self.size:\n",
    "           self.buffer.append(item)\n",
    "        else:\n",
    "            self.buffer[self.next_id] = item\n",
    "        self.next_id = (self.next_id + 1) % self.size\n",
    "\n",
    "    def sample(self, batch_size):\n",
    "        idxs = np.random.choice(len(self.buffer), batch_size)\n",
    "        samples = [self.buffer[i] for i in idxs]\n",
    "        states, actions, rewards, next_states, done_flags = list(zip(*samples))\n",
    "        return np.array(states), np.array(actions), np.array(rewards), np.array(next_states), np.array(done_flags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "Qa-6uMiwGyLm"
   },
   "outputs": [],
   "source": [
    "def play_and_record(start_state, agent, env, exp_replay, n_steps=1):\n",
    "\n",
    "    s = start_state\n",
    "    sum_rewards = 0\n",
    "    # Play the game for n_steps and record transitions in buffer\n",
    "    for i in range(n_steps):\n",
    "        qvalues = agent.get_qvalues([s])\n",
    "        a = agent.sample_actions(qvalues)[0]\n",
    "        next_s, r, terminated, truncated, _ = env.step(a)\n",
    "        sum_rewards += r\n",
    "        done = terminated or truncated\n",
    "        exp_replay.add(s, a, r, next_s, done)\n",
    "        if terminated:\n",
    "            s, _ = env.reset(seed=i)\n",
    "        else:\n",
    "            s = next_s\n",
    "\n",
    "    return sum_rewards, s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jEd-TSuBGyLm"
   },
   "source": [
    "### Target network\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "k4T242C8GyLm",
    "outputId": "74423ff1-64b8-4c0a-a92e-1b10a0639fa5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_network = DQNAgent(agent.state_shape, agent.n_actions, epsilon=0.5).to(device)\n",
    "target_network.load_state_dict(agent.state_dict())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Fab0ZhimGyLm"
   },
   "source": [
    "### Learning with DQN\n",
    "Here we write a function similar to tabular q-learning. We will calculate average TD error per batch using the equation:\n",
    "\n",
    "$$ L =  \\frac{1}{N} \\sum_{i=1}^{N} \\left[ r_i + \\left( (1-done_i) . \\gamma .  \\max_{a'} \\hat{q}(s'_i,a';w^-_t) \\right) – \\hat{q}_{w_t}(s_i,a_i;w_t) \\right]^2$$\n",
    "\n",
    "\n",
    "$$ \\nabla_{w_t} L =   - \\frac{1}{N} \\sum_{i=1}^{N} \\left[ r_i + \\left( (1-done_i) . \\gamma .  \\max_{a'} \\hat{q}(s'_i,a';w^-_t) \\right) – \\hat{q}(s_i,a_i;w_t) \\right] \\nabla \\hat{q}_{w_t}(s_i,a_i;w_t)$$\n",
    "\n",
    "\n",
    "$\\hat{q}(s',a';w^{-})$ is calculated using target network whose weights are held constant and refreshed periodically from the agent learning network.\n",
    "\n",
    "Target is given by following:\n",
    "* non terminal state: $r_i +  \\gamma .  \\max\\limits_{a'} \\hat{q}(s'_i,a';w^-_t)$\n",
    "* terminal state: $ r_i $\n",
    "\n",
    "We then carryout back propagation through the agent network to update the weights using equation below:\n",
    "\n",
    "\n",
    "$$\n",
    "w_{t+1} \\leftarrow w_t - \\alpha . \\nabla_{w_t}L$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "wJY5rm21GyLm"
   },
   "outputs": [],
   "source": [
    "def compute_td_loss(agent, target_network, states, actions, rewards, next_states, done_flags,\n",
    "                    gamma=0.99, device=device):\n",
    "\n",
    "    # convert numpy array to torch tensors\n",
    "    states = torch.tensor(states, device=device, dtype=torch.float)\n",
    "    actions = torch.tensor(actions, device=device, dtype=torch.long)\n",
    "    rewards = torch.tensor(rewards, device=device, dtype=torch.float)\n",
    "    next_states = torch.tensor(next_states, device=device, dtype=torch.float)\n",
    "    done_flags = torch.tensor(done_flags.astype('float32'),device=device,dtype=torch.float)\n",
    "\n",
    "    # get q-values for all actions in current states\n",
    "    # use agent network\n",
    "    predicted_qvalues = agent(states)\n",
    "\n",
    "    # compute q-values for all actions in next states\n",
    "    # use target network\n",
    "    predicted_next_qvalues = target_network(next_states)\n",
    "\n",
    "    # select q-values for chosen actions\n",
    "    predicted_qvalues_for_actions = predicted_qvalues[range(\n",
    "        len(actions)), actions]\n",
    "\n",
    "    # compute Qmax(next_states, actions) using predicted next q-values\n",
    "    next_state_values,_ = torch.max(predicted_next_qvalues, dim=1)\n",
    "\n",
    "    # compute \"target q-values\"\n",
    "    target_qvalues_for_actions = rewards + gamma * next_state_values * (1-done_flags)\n",
    "\n",
    "    # mean squared error loss to minimize\n",
    "    loss = torch.mean((predicted_qvalues_for_actions -\n",
    "                       target_qvalues_for_actions.detach()) ** 2)\n",
    "\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZyICZqJiGyLn"
   },
   "source": [
    "### Main loop\n",
    "\n",
    "We now carryout the training on DQN setup above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "_CcGovXXGyLn"
   },
   "outputs": [],
   "source": [
    "from tqdm import trange\n",
    "from IPython.display import clear_output\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "McQ42lmLGyLn",
    "outputId": "84524d16-f589-4c46-89a3-caa7e746ae00"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x7f2449d1be90>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set a seed\n",
    "seed = 13\n",
    "random.seed(seed)\n",
    "np.random.seed(seed)\n",
    "torch.manual_seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9cYOhoG1GyLn",
    "outputId": "23a257f2-0a73-4727-9946-edb60bade321"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#setup env and agent and target networks\n",
    "env = make_env(env_name)\n",
    "state_dim = env.observation_space.shape\n",
    "n_actions = env.action_space.n\n",
    "state, _ = env.reset(seed=seed)\n",
    "\n",
    "agent = DQNAgent(state_dim, n_actions, epsilon=1).to(device)\n",
    "target_network = DQNAgent(state_dim, n_actions, epsilon=1).to(device)\n",
    "target_network.load_state_dict(agent.state_dict())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wVHA9dDpGyLn",
    "outputId": "de97ba96-b37f-40db-cf31-8cb231c99203"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000\n"
     ]
    }
   ],
   "source": [
    "# let us fill experience replay with some samples using full random policy\n",
    "exp_replay = ReplayBuffer(10**4)\n",
    "for i in range(100):\n",
    "    play_and_record(state, agent, env, exp_replay, n_steps=10**2)\n",
    "    if len(exp_replay) == 10**4:\n",
    "        break\n",
    "print(len(exp_replay))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "mc4F6ZzhGyLn"
   },
   "outputs": [],
   "source": [
    "#setup some parameters for training\n",
    "timesteps_per_epoch = 1\n",
    "batch_size = 32\n",
    "\n",
    "total_steps = 100\n",
    "# total_steps = 3 * 10**6\n",
    "# We will train only for a sample of 100 steps\n",
    "# To train the full network on a CPU will take hours.\n",
    "# infact even GPU training will be fairly long\n",
    "# Those who have access to powerful machines with GPU could\n",
    "# try training it over 3-5 million steps or so\n",
    "\n",
    "\n",
    "#init Optimizer\n",
    "opt = torch.optim.Adam(agent.parameters(), lr=1e-4)\n",
    "\n",
    "# set exploration epsilon\n",
    "start_epsilon = 1\n",
    "end_epsilon = 0.05\n",
    "eps_decay_final_step = 1 * 10**6\n",
    "\n",
    "# setup some frequency for logging and updating target network\n",
    "loss_freq = 20\n",
    "refresh_target_network_freq = 20 # change this to 100 if total_steps = 3*10**6\n",
    "eval_freq = 10 #change this to 1000 if total_steps = 3*10**6\n",
    "\n",
    "# to clip the gradients\n",
    "max_grad_norm = 5000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "HeZf9BcbGyLn"
   },
   "outputs": [],
   "source": [
    "mean_rw_history = []\n",
    "td_loss_history = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "GA5BwY3TGyLo"
   },
   "outputs": [],
   "source": [
    "def epsilon_schedule(start_eps, end_eps, step, final_step):\n",
    "    return start_eps + (end_eps-start_eps)*min(step, final_step)/final_step\n",
    "\n",
    "def smoothen(values):\n",
    "    kernel = gaussian(100, std=100)\n",
    "    kernel = kernel / np.sum(kernel)\n",
    "    return convolve(values, kernel, 'valid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 505
    },
    "id": "VcRPKh5oGyLo",
    "outputId": "a92e0bd0-d2d0-4079-e048-c5e7b1a8c52a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "buffer size = 10000, epsilon = 0.99991\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████| 101/101 [02:28<00:00,  1.47s/it]\n"
     ]
    }
   ],
   "source": [
    "state, _ = env.reset(seed=seed)\n",
    "for step in trange(total_steps + 1):\n",
    "\n",
    "    # reduce exploration as we progress\n",
    "    agent.epsilon = epsilon_schedule(start_epsilon, end_epsilon, step, eps_decay_final_step)\n",
    "\n",
    "    # take timesteps_per_epoch and update experience replay buffer\n",
    "    _, state = play_and_record(state, agent, env, exp_replay, timesteps_per_epoch)\n",
    "\n",
    "    # train by sampling batch_size of data from experience replay\n",
    "    states, actions, rewards, next_states, done_flags = exp_replay.sample(batch_size)\n",
    "\n",
    "\n",
    "    # loss = <compute TD loss>\n",
    "    loss = compute_td_loss(agent, target_network,\n",
    "                           states, actions, rewards, next_states, done_flags,\n",
    "                           gamma=0.99,\n",
    "                           device=device)\n",
    "\n",
    "    loss.backward()\n",
    "    grad_norm = nn.utils.clip_grad_norm_(agent.parameters(), max_grad_norm)\n",
    "    opt.step()\n",
    "    opt.zero_grad()\n",
    "\n",
    "    if step % loss_freq == 0:\n",
    "        td_loss_history.append(loss.data.cpu().item())\n",
    "\n",
    "    if step % refresh_target_network_freq == 0:\n",
    "        # Load agent weights into target_network\n",
    "        target_network.load_state_dict(agent.state_dict())\n",
    "\n",
    "    if step % eval_freq == 0:\n",
    "        # eval the agent\n",
    "        mean_rw_history.append(evaluate(\n",
    "            make_env(env_name), agent, n_games=3, greedy=True, t_max=1000)\n",
    "        )\n",
    "\n",
    "        clear_output(True)\n",
    "        print(\"buffer size = %i, epsilon = %.5f\" %\n",
    "              (len(exp_replay), agent.epsilon))\n",
    "\n",
    "        plt.figure(figsize=[16, 5])\n",
    "        plt.subplot(1, 2, 1)\n",
    "        plt.title(\"Mean return per episode\")\n",
    "        plt.plot(mean_rw_history)\n",
    "        plt.grid()\n",
    "\n",
    "        assert not np.isnan(td_loss_history[-1])\n",
    "        plt.subplot(1, 2, 2)\n",
    "        plt.title(\"TD loss history (smoothened)\")\n",
    "        plt.plot(smoothen(td_loss_history))\n",
    "        plt.grid()\n",
    "\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kaDJ58KjGyLp",
    "outputId": "e7ff2d29-3d07-4243-de07-f2d765a0b9be"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final score: 0.0\n"
     ]
    }
   ],
   "source": [
    "final_score = evaluate(\n",
    "  make_env(env_name),\n",
    "  agent, n_games=1, greedy=True, t_max=1000\n",
    ")\n",
    "print('final score:', final_score)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "K31XETukGyLp"
   },
   "source": [
    "The training for 100 steps will not produce any result. It may be close to 100,000 steps or so before you see any semblance of learning by agent."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YiCGR7LcGyLp"
   },
   "source": [
    "**Let us record a video of trained agent**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "Wvs01wwPGyLp"
   },
   "outputs": [],
   "source": [
    "# Helper function to record videos\n",
    "def record_video(env_id, make_env, video_folder, video_length, agent):\n",
    "\n",
    "    vec_env = DummyVecEnv([lambda: make_env(env_id, clip_rewards=False)])\n",
    "    env_id_suffix = env_id.split(\"/\")[-1]\n",
    "    # Record the video starting at the first step\n",
    "    vec_env = VecVideoRecorder(vec_env, video_folder,\n",
    "                           record_video_trigger=lambda x: x == 0, video_length=video_length,\n",
    "                           name_prefix=f\"{type(agent).__name__}-{env_id_suffix}\")\n",
    "\n",
    "    obs = vec_env.reset()\n",
    "    print(obs.shape)\n",
    "    for _ in range(video_length + 1):\n",
    "        qvalues = agent.get_qvalues(obs)\n",
    "        action = qvalues.argmax(axis=-1)\n",
    "        obs, _, dones, _ = vec_env.step(action)\n",
    "    # video filename\n",
    "    file_path = \"./\"+video_folder+vec_env.video_recorder.path.split(\"/\")[-1]\n",
    "    # Save the video\n",
    "    vec_env.close()\n",
    "    return file_path\n",
    "\n",
    "def play_video(file_path):\n",
    "    mp4 = open(file_path, 'rb').read()\n",
    "    data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n",
    "    return HTML(\"\"\"\n",
    "        <video width=400 controls>\n",
    "              <source src=\"%s\" type=\"video/mp4\">\n",
    "        </video>\n",
    "        \"\"\" % data_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 722
    },
    "id": "kAST8Tk6GyLq",
    "outputId": "65eb05b6-b31d-4eba-ccc5-854ceb1b55b9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 4, 84, 84)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nsanghi/sandbox/apress/drl-2ed/venv39/lib/python3.9/site-packages/gymnasium/utils/passive_env_checker.py:335: UserWarning: \u001b[33mWARN: No render fps was declared in the environment (env.metadata['render_fps'] is None or not defined), rendering may occur at inconsistent fps.\u001b[0m\n",
      "  logger.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving video to /home/nsanghi/sandbox/apress/drl-2ed/chapter6/logs/videos/pytorch/6_b/DQNAgent-BreakoutNoFrameskip-v4-step-0-to-step-500.mp4\n",
      "Moviepy - Building video /home/nsanghi/sandbox/apress/drl-2ed/chapter6/logs/videos/pytorch/6_b/DQNAgent-BreakoutNoFrameskip-v4-step-0-to-step-500.mp4.\n",
      "Moviepy - Writing video /home/nsanghi/sandbox/apress/drl-2ed/chapter6/logs/videos/pytorch/6_b/DQNAgent-BreakoutNoFrameskip-v4-step-0-to-step-500.mp4\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                                        "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Moviepy - Done !\n",
      "Moviepy - video ready /home/nsanghi/sandbox/apress/drl-2ed/chapter6/logs/videos/pytorch/6_b/DQNAgent-BreakoutNoFrameskip-v4-step-0-to-step-500.mp4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    },
    {
     "data": {
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type=\"video/mp4\">\n",
       "        </video>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "video_folder = \"logs/videos/pytorch/6_b/\"\n",
    "video_length = 500\n",
    "\n",
    "video_file = record_video(env_name, make_env, video_folder, video_length, agent)\n",
    "play_video(video_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "akNKKLq7GyLq"
   },
   "source": [
    "### Train using RL_Zoo3\n",
    "We will also be using Weights and Biases to records the run and other experiment details. For this we need to login using the wandb api key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 121
    },
    "id": "RqCXbGviGyLq",
    "outputId": "607fcbd6-b8f4-414c-a1d0-2ac40577d39c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mnsanghi\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import wandb\n",
    "wandb.login()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**IMPORTANT**\n",
    "In case you face a login error with `wandb.login()`, please run the following cell by setting first your wandb api key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"WANDB_API_KEY\"] = \"Your WANDB_API_KEY......\"\n",
    "import wandb\n",
    "wandb.login()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kk8qC53RGyLq"
   },
   "source": [
    "#### Train agent\n",
    "\n",
    "For the current version we will use the same environment from Version 4. You can read more about the available environments [here](https://gymnasium.farama.org/environments/atari/breakout/) and [here](https://gymnasium.farama.org/environments/atari/). The difference is in terms of default values of `repeat_action_probability` and `frameskip`. For RL ZOO 3 we will use `BreakoutNoFrameskip-v4`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "psKnUTPqGyLq"
   },
   "source": [
    "#### Sample run with CPU\n",
    "To keep run time within bounds you can use this script where the number of training steps have been kept small\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sQyMl2WNGyLq",
    "outputId": "7dfe282f-fdf8-4cac-e05e-2de177366beb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-04-14 17:50:06.745933: I tensorflow/core/util/port.cc:111] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2024-04-14 17:50:06.749323: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-04-14 17:50:06.788459: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-04-14 17:50:06.788538: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-04-14 17:50:06.788621: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-04-14 17:50:06.799705: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-04-14 17:50:06.800007: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-04-14 17:50:07.396927: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
      "========== BreakoutNoFrameskip-v4 ==========\n",
      "Seed: 2363843725\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mnsanghi\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: wandb version 0.16.6 is available!  To upgrade, please run:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:  $ pip install wandb --upgrade\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.15.12\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/home/nsanghi/sandbox/apress/drl-2ed/chapter6/wandb/run-20240414_175010-mqa2qg3z\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mBreakoutNoFrameskip-v4__dqn__2363843725__1713097209\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/nsanghi/dqn-breakout\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/nsanghi/dqn-breakout/runs/mqa2qg3z\u001b[0m\n",
      "Loading hyperparameters from: /home/nsanghi/sandbox/apress/drl-2ed/venv39/lib/python3.9/site-packages/rl_zoo3/hyperparams/dqn.yml\n",
      "Default hyperparameters for environment (ones being tuned will be overridden):\n",
      "OrderedDict([('batch_size', 32),\n",
      "             ('buffer_size', 100000),\n",
      "             ('env_wrapper',\n",
      "              ['stable_baselines3.common.atari_wrappers.AtariWrapper']),\n",
      "             ('exploration_final_eps', 0.01),\n",
      "             ('exploration_fraction', 0.1),\n",
      "             ('frame_stack', 4),\n",
      "             ('gradient_steps', 1),\n",
      "             ('learning_rate', 0.0001),\n",
      "             ('learning_starts', 100000),\n",
      "             ('n_timesteps', 10000000.0),\n",
      "             ('optimize_memory_usage', False),\n",
      "             ('policy', 'CnnPolicy'),\n",
      "             ('target_update_interval', 1000),\n",
      "             ('train_freq', 4)])\n",
      "Using 1 environments\n",
      "Overwriting n_timesteps with n=500000\n",
      "Creating test environment\n",
      "A.L.E: Arcade Learning Environment (version 0.8.1+53f58b7)\n",
      "[Powered by Stella]\n",
      "Stacking 4 frames\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "Stacking 4 frames\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "Using cpu device\n",
      "Log path: logs/6_c/rlzoo3//dqn/BreakoutNoFrameskip-v4_4\n",
      "Logging to runs/BreakoutNoFrameskip-v4__dqn__2363843725__1713097209/BreakoutNoFrameskip-v4/DQN_1\n",
      "\u001b[2K\u001b[35m   0%\u001b[0m \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m202/500,000 \u001b[0m [ \u001b[33m0:00:00\u001b[0m < \u001b[36m0:09:13\u001b[0m , \u001b[31m905 it/s\u001b[0m ]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-04-14 17:50:12.208233: I tensorflow/core/util/port.cc:111] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2024-04-14 17:50:12.210131: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-04-14 17:50:12.244716: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-04-14 17:50:12.244778: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-04-14 17:50:12.244796: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-04-14 17:50:12.251466: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-04-14 17:50:12.253765: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K\u001b[35m   0%\u001b[0m \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m913/500,000 \u001b[0m [ \u001b[33m0:00:01\u001b[0m < \u001b[36m0:08:31\u001b[0m , \u001b[31m977 it/s\u001b[0m ]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-04-14 17:50:12.896408: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2KEval num_timesteps=10000, episode_reward=2.30 +/- 2.839,933/500,000 \u001b[0m [ \u001b[33m0:00:12\u001b[0m < \u001b[36m0:07:50\u001b[0m , \u001b[31m1,044 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 879.20 +/- 435.61━━━━━━━\u001b[0m \u001b[32m9,933/500,000 \u001b[0m [ \u001b[33m0:00:12\u001b[0m < \u001b[36m0:07:50\u001b[0m , \u001b[31m1,044 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------━━━━━━\u001b[0m \u001b[32m9,933/500,000 \u001b[0m [ \u001b[33m0:00:12\u001b[0m < \u001b[36m0:07:50\u001b[0m , \u001b[31m1,044 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 879      |\n",
      "|    mean_reward      | 2.3      |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.802    |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 10000    |\n",
      "----------------------------------\n",
      "\u001b[2KNew best mean reward!━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9,933/500,000 \u001b[0m [ \u001b[33m0:00:12\u001b[0m < \u001b[36m0:07:50\u001b[0m , \u001b[31m1,044 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13,429/500,000 \u001b[0m [ \u001b[33m0:00:15\u001b[0m < \u001b[36m0:09:33\u001b[0m , \u001b[31m850 it/s\u001b[0m ]\n",
      "| rollout/            |          |\n",
      "|    ep_len_mean      | 745      |\n",
      "|    ep_rew_mean      | 1.45     |\n",
      "|    exploration_rate | 0.732    |\n",
      "| time/               |          |\n",
      "|    episodes         | 400      |\n",
      "|    fps              | 844      |\n",
      "|    time_elapsed     | 16       |\n",
      "|    total_timesteps  | 13552    |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=20000, episode_reward=2.80 +/- 4.04\u001b[0m \u001b[32m19,927/500,000 \u001b[0m [ \u001b[33m0:00:24\u001b[0m < \u001b[36m0:08:52\u001b[0m , \u001b[31m903 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 783.10 +/- 364.35━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m19,927/500,000 \u001b[0m [ \u001b[33m0:00:24\u001b[0m < \u001b[36m0:08:52\u001b[0m , \u001b[31m903 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m19,927/500,000 \u001b[0m [ \u001b[33m0:00:24\u001b[0m < \u001b[36m0:08:52\u001b[0m , \u001b[31m903 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 783      |\n",
      "|    mean_reward      | 2.8      |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.604    |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 20000    |\n",
      "----------------------------------\n",
      "\u001b[2KNew best mean reward!\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m19,927/500,000 \u001b[0m [ \u001b[33m0:00:24\u001b[0m < \u001b[36m0:08:52\u001b[0m , \u001b[31m903 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m27,109/500,000 \u001b[0m [ \u001b[33m0:00:32\u001b[0m < \u001b[36m0:09:24\u001b[0m , \u001b[31m839 it/s\u001b[0m ]\n",
      "| rollout/            |          |\n",
      "|    ep_len_mean      | 748      |\n",
      "|    ep_rew_mean      | 1.35     |\n",
      "|    exploration_rate | 0.46     |\n",
      "| time/               |          |\n",
      "|    episodes         | 800      |\n",
      "|    fps              | 842      |\n",
      "|    time_elapsed     | 32       |\n",
      "|    total_timesteps  | 27256    |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=30000, episode_reward=1.20 +/- 3.28━━━━━━━━━\u001b[0m \u001b[32m29,920/500,000 \u001b[0m [ \u001b[33m0:00:37\u001b[0m < \u001b[36m0:09:28\u001b[0m , \u001b[31m829 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 595.30 +/- 168.6090m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m29,920/500,000 \u001b[0m [ \u001b[33m0:00:37\u001b[0m < \u001b[36m0:09:28\u001b[0m , \u001b[31m829 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------0m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m29,920/500,000 \u001b[0m [ \u001b[33m0:00:37\u001b[0m < \u001b[36m0:09:28\u001b[0m , \u001b[31m829 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 595      |\n",
      "|    mean_reward      | 1.2      |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.406    |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 30000    |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=40000, episode_reward=3.40 +/- 4.98━━━━━━━━━\u001b[0m \u001b[32m39,904/500,000 \u001b[0m [ \u001b[33m0:00:50\u001b[0m < \u001b[36m0:09:18\u001b[0m , \u001b[31m826 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 708.30 +/- 258.0890m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m39,904/500,000 \u001b[0m [ \u001b[33m0:00:50\u001b[0m < \u001b[36m0:09:18\u001b[0m , \u001b[31m826 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------0m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m39,904/500,000 \u001b[0m [ \u001b[33m0:00:50\u001b[0m < \u001b[36m0:09:18\u001b[0m , \u001b[31m826 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 708      |\n",
      "|    mean_reward      | 3.4      |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.208    |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 40000    |\n",
      "----------------------------------\n",
      "\u001b[2KNew best mean reward!\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m39,904/500,000 \u001b[0m [ \u001b[33m0:00:50\u001b[0m < \u001b[36m0:09:18\u001b[0m , \u001b[31m826 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40,191/500,000 \u001b[0m [ \u001b[33m0:00:50\u001b[0m < \u001b[36m0:10:40\u001b[0m , \u001b[31m719 it/s\u001b[0m ]\n",
      "| rollout/            |          |\n",
      "|    ep_len_mean      | 730      |\n",
      "|    ep_rew_mean      | 1.29     |\n",
      "|    exploration_rate | 0.201    |\n",
      "| time/               |          |\n",
      "|    episodes         | 1200     |\n",
      "|    fps              | 790      |\n",
      "|    time_elapsed     | 51       |\n",
      "|    total_timesteps  | 40341    |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=50000, episode_reward=3.30 +/- 5.04━━━━━━━━━\u001b[0m \u001b[32m49,961/500,000 \u001b[0m [ \u001b[33m0:01:05\u001b[0m < \u001b[36m0:09:34\u001b[0m , \u001b[31m785 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 698.90 +/- 261.12[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49,961/500,000 \u001b[0m [ \u001b[33m0:01:05\u001b[0m < \u001b[36m0:09:34\u001b[0m , \u001b[31m785 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49,961/500,000 \u001b[0m [ \u001b[33m0:01:05\u001b[0m < \u001b[36m0:09:34\u001b[0m , \u001b[31m785 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 699      |\n",
      "|    mean_reward      | 3.3      |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 50000    |\n",
      "----------------------------------\n",
      "\u001b[2K----------------------------------0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m52,926/500,000 \u001b[0m [ \u001b[33m0:01:08\u001b[0m < \u001b[36m0:10:25\u001b[0m , \u001b[31m716 it/s\u001b[0m ]\n",
      "| rollout/            |          |\n",
      "|    ep_len_mean      | 685      |\n",
      "|    ep_rew_mean      | 1.03     |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    episodes         | 1600     |\n",
      "|    fps              | 770      |\n",
      "|    time_elapsed     | 68       |\n",
      "|    total_timesteps  | 52976    |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=60000, episode_reward=2.20 +/- 4.40━━━━━━━━━\u001b[0m \u001b[32m59,957/500,000 \u001b[0m [ \u001b[33m0:01:19\u001b[0m < \u001b[36m0:10:33\u001b[0m , \u001b[31m695 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 639.10 +/- 226.14[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m59,957/500,000 \u001b[0m [ \u001b[33m0:01:19\u001b[0m < \u001b[36m0:10:33\u001b[0m , \u001b[31m695 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m59,957/500,000 \u001b[0m [ \u001b[33m0:01:19\u001b[0m < \u001b[36m0:10:33\u001b[0m , \u001b[31m695 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 639      |\n",
      "|    mean_reward      | 2.2      |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 60000    |\n",
      "----------------------------------\n",
      "\u001b[2K----------------------------------[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65,738/500,000 \u001b[0m [ \u001b[33m0:01:25\u001b[0m < \u001b[36m0:10:31\u001b[0m , \u001b[31m689 it/s\u001b[0m ]\n",
      "| rollout/            |          |\n",
      "|    ep_len_mean      | 706      |\n",
      "|    ep_rew_mean      | 1.19     |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    episodes         | 2000     |\n",
      "|    fps              | 763      |\n",
      "|    time_elapsed     | 86       |\n",
      "|    total_timesteps  | 65755    |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=70000, episode_reward=5.50 +/- 5.50━━━━━━━━━\u001b[0m \u001b[32m69,992/500,000 \u001b[0m [ \u001b[33m0:01:34\u001b[0m < \u001b[36m0:10:25\u001b[0m , \u001b[31m689 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 806.90 +/- 291.33\u001b[90m━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m69,992/500,000 \u001b[0m [ \u001b[33m0:01:34\u001b[0m < \u001b[36m0:10:25\u001b[0m , \u001b[31m689 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------[90m━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m69,992/500,000 \u001b[0m [ \u001b[33m0:01:34\u001b[0m < \u001b[36m0:10:25\u001b[0m , \u001b[31m689 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 807      |\n",
      "|    mean_reward      | 5.5      |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 70000    |\n",
      "----------------------------------\n",
      "\u001b[2KNew best mean reward!0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m69,992/500,000 \u001b[0m [ \u001b[33m0:01:34\u001b[0m < \u001b[36m0:10:25\u001b[0m , \u001b[31m689 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79,195/500,000 \u001b[0m [ \u001b[33m0:01:44\u001b[0m < \u001b[36m0:09:51\u001b[0m , \u001b[31m712 it/s\u001b[0m ]\n",
      "| rollout/            |          |\n",
      "|    ep_len_mean      | 759      |\n",
      "|    ep_rew_mean      | 1.56     |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    episodes         | 2400     |\n",
      "|    fps              | 758      |\n",
      "|    time_elapsed     | 104      |\n",
      "|    total_timesteps  | 79251    |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=80000, episode_reward=1.10 +/- 3.30━━━━━━━━━\u001b[0m \u001b[32m79,907/500,000 \u001b[0m [ \u001b[33m0:01:47\u001b[0m < \u001b[36m0:09:50\u001b[0m , \u001b[31m713 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 585.40 +/- 175.46\u001b[90m━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79,907/500,000 \u001b[0m [ \u001b[33m0:01:47\u001b[0m < \u001b[36m0:09:50\u001b[0m , \u001b[31m713 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------[90m━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79,907/500,000 \u001b[0m [ \u001b[33m0:01:47\u001b[0m < \u001b[36m0:09:50\u001b[0m , \u001b[31m713 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 585      |\n",
      "|    mean_reward      | 1.1      |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 80000    |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=90000, episode_reward=2.20 +/- 4.40━━━━━━━━━\u001b[0m \u001b[32m89,952/500,000 \u001b[0m [ \u001b[33m0:02:01\u001b[0m < \u001b[36m0:09:14\u001b[0m , \u001b[31m741 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 645.20 +/- 228.02m\u001b[90m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m89,952/500,000 \u001b[0m [ \u001b[33m0:02:01\u001b[0m < \u001b[36m0:09:14\u001b[0m , \u001b[31m741 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------\u001b[90m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m89,952/500,000 \u001b[0m [ \u001b[33m0:02:01\u001b[0m < \u001b[36m0:09:14\u001b[0m , \u001b[31m741 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 645      |\n",
      "|    mean_reward      | 2.2      |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 90000    |\n",
      "----------------------------------\n",
      "\u001b[2K----------------------------------\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m91,845/500,000 \u001b[0m [ \u001b[33m0:02:03\u001b[0m < \u001b[36m0:08:56\u001b[0m , \u001b[31m762 it/s\u001b[0m ]\n",
      "| rollout/            |          |\n",
      "|    ep_len_mean      | 708      |\n",
      "|    ep_rew_mean      | 1.17     |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    episodes         | 2800     |\n",
      "|    fps              | 744      |\n",
      "|    time_elapsed     | 123      |\n",
      "|    total_timesteps  | 91929    |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=100000, episode_reward=4.40 +/- 5.39━━━━━━━━\u001b[0m \u001b[32m99,974/500,000 \u001b[0m [ \u001b[33m0:02:14\u001b[0m < \u001b[36m0:08:29\u001b[0m , \u001b[31m787 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 755.90 +/- 279.42m\u001b[90m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m99,974/500,000 \u001b[0m [ \u001b[33m0:02:14\u001b[0m < \u001b[36m0:08:29\u001b[0m , \u001b[31m787 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------\u001b[90m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m99,974/500,000 \u001b[0m [ \u001b[33m0:02:14\u001b[0m < \u001b[36m0:08:29\u001b[0m , \u001b[31m787 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 756      |\n",
      "|    mean_reward      | 4.4      |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 100000   |\n",
      "----------------------------------\n",
      "\u001b[2K----------------------------------╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m104,737/500,000 \u001b[0m [ \u001b[33m0:03:00\u001b[0m < \u001b[36m1:07:17\u001b[0m , \u001b[31m98 it/s\u001b[0m ]\n",
      "| rollout/            |          |\n",
      "|    ep_len_mean      | 706      |\n",
      "|    ep_rew_mean      | 1.2      |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    episodes         | 3200     |\n",
      "|    fps              | 578      |\n",
      "|    time_elapsed     | 181      |\n",
      "|    total_timesteps  | 104753   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.000512 |\n",
      "|    n_updates        | 1188     |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=110000, episode_reward=1.10 +/- 3.30━━━━━━━━\u001b[0m \u001b[32m109,990/500,000 \u001b[0m [ \u001b[33m0:03:59\u001b[0m < \u001b[36m1:08:10\u001b[0m , \u001b[31m95 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 587.60 +/- 171.930m\u001b[90m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m109,990/500,000 \u001b[0m [ \u001b[33m0:03:59\u001b[0m < \u001b[36m1:08:10\u001b[0m , \u001b[31m95 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------m\u001b[90m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m109,990/500,000 \u001b[0m [ \u001b[33m0:03:59\u001b[0m < \u001b[36m1:08:10\u001b[0m , \u001b[31m95 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 588      |\n",
      "|    mean_reward      | 1.1      |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 110000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.000304 |\n",
      "|    n_updates        | 2499     |\n",
      "----------------------------------\n",
      "\u001b[2K----------------------------------╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m117,758/500,000 \u001b[0m [ \u001b[33m0:05:29\u001b[0m < \u001b[36m1:20:45\u001b[0m , \u001b[31m79 it/s\u001b[0m ]\n",
      "| rollout/            |          |\n",
      "|    ep_len_mean      | 714      |\n",
      "|    ep_rew_mean      | 1.31     |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    episodes         | 3600     |\n",
      "|    fps              | 357      |\n",
      "|    time_elapsed     | 329      |\n",
      "|    total_timesteps  | 117762   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.000291 |\n",
      "|    n_updates        | 4440     |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=120000, episode_reward=1.20 +/- 1.83━━━━━━━━\u001b[0m \u001b[32m119,997/500,000 \u001b[0m [ \u001b[33m0:06:01\u001b[0m < \u001b[36m1:18:51\u001b[0m , \u001b[31m80 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 754.80 +/- 348.770m\u001b[90m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m119,997/500,000 \u001b[0m [ \u001b[33m0:06:01\u001b[0m < \u001b[36m1:18:51\u001b[0m , \u001b[31m80 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------m\u001b[90m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m119,997/500,000 \u001b[0m [ \u001b[33m0:06:01\u001b[0m < \u001b[36m1:18:51\u001b[0m , \u001b[31m80 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 755      |\n",
      "|    mean_reward      | 1.2      |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 120000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.000139 |\n",
      "|    n_updates        | 4999     |\n",
      "----------------------------------\n",
      "\u001b[2K----------------------------------m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129,949/500,000 \u001b[0m [ \u001b[33m0:08:04\u001b[0m < \u001b[36m1:15:18\u001b[0m , \u001b[31m82 it/s\u001b[0m ]\n",
      "| rollout/            |          |\n",
      "|    ep_len_mean      | 690      |\n",
      "|    ep_rew_mean      | 1.05     |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    episodes         | 4000     |\n",
      "|    fps              | 268      |\n",
      "|    time_elapsed     | 484      |\n",
      "|    total_timesteps  | 129958   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.00222  |\n",
      "|    n_updates        | 7489     |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=130000, episode_reward=0.10 +/- 0.30━━━━━━━━\u001b[0m \u001b[32m129,993/500,000 \u001b[0m [ \u001b[33m0:08:08\u001b[0m < \u001b[36m1:15:11\u001b[0m , \u001b[31m82 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 539.50 +/- 33.55\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129,993/500,000 \u001b[0m [ \u001b[33m0:08:08\u001b[0m < \u001b[36m1:15:11\u001b[0m , \u001b[31m82 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------0m\u001b[90m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129,993/500,000 \u001b[0m [ \u001b[33m0:08:08\u001b[0m < \u001b[36m1:15:11\u001b[0m , \u001b[31m82 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 540      |\n",
      "|    mean_reward      | 0.1      |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 130000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.00306  |\n",
      "|    n_updates        | 7499     |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=140000, episode_reward=1.80 +/- 3.60━━━━━━━━\u001b[0m \u001b[32m139,997/500,000 \u001b[0m [ \u001b[33m0:10:24\u001b[0m < \u001b[36m1:18:07\u001b[0m , \u001b[31m77 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 754.20 +/- 444.96[0m\u001b[90m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139,997/500,000 \u001b[0m [ \u001b[33m0:10:24\u001b[0m < \u001b[36m1:18:07\u001b[0m , \u001b[31m77 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------0m\u001b[90m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139,997/500,000 \u001b[0m [ \u001b[33m0:10:24\u001b[0m < \u001b[36m1:18:07\u001b[0m , \u001b[31m77 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 754      |\n",
      "|    mean_reward      | 1.8      |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 140000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0049   |\n",
      "|    n_updates        | 9999     |\n",
      "----------------------------------\n",
      "\u001b[2K----------------------------------0m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m145,149/500,000 \u001b[0m [ \u001b[33m0:11:27\u001b[0m < \u001b[36m1:10:38\u001b[0m , \u001b[31m84 it/s\u001b[0m ]\n",
      "| rollout/            |          |\n",
      "|    ep_len_mean      | 809      |\n",
      "|    ep_rew_mean      | 1.93     |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    episodes         | 4400     |\n",
      "|    fps              | 211      |\n",
      "|    time_elapsed     | 687      |\n",
      "|    total_timesteps  | 145154   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.00388  |\n",
      "|    n_updates        | 11288    |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=150000, episode_reward=4.70 +/- 4.34━━━━━━━━\u001b[0m \u001b[32m149,993/500,000 \u001b[0m [ \u001b[33m0:12:39\u001b[0m < \u001b[36m1:14:17\u001b[0m , \u001b[31m79 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 1120.00 +/- 527.40[0m\u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149,993/500,000 \u001b[0m [ \u001b[33m0:12:39\u001b[0m < \u001b[36m1:14:17\u001b[0m , \u001b[31m79 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------[0m\u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149,993/500,000 \u001b[0m [ \u001b[33m0:12:39\u001b[0m < \u001b[36m1:14:17\u001b[0m , \u001b[31m79 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 1.12e+03 |\n",
      "|    mean_reward      | 4.7      |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 150000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0025   |\n",
      "|    n_updates        | 12499    |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=160000, episode_reward=4.70 +/- 2.57━━━━━━━━\u001b[0m \u001b[32m159,997/500,000 \u001b[0m [ \u001b[33m0:15:00\u001b[0m < \u001b[36m1:10:36\u001b[0m , \u001b[31m80 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 1141.10 +/- 298.33[0m\u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m159,997/500,000 \u001b[0m [ \u001b[33m0:15:00\u001b[0m < \u001b[36m1:10:36\u001b[0m , \u001b[31m80 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------[0m\u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m159,997/500,000 \u001b[0m [ \u001b[33m0:15:00\u001b[0m < \u001b[36m1:10:36\u001b[0m , \u001b[31m80 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 1.14e+03 |\n",
      "|    mean_reward      | 4.7      |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 160000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.00149  |\n",
      "|    n_updates        | 14999    |\n",
      "----------------------------------\n",
      "\u001b[2K----------------------------------90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m165,653/500,000 \u001b[0m [ \u001b[33m0:16:10\u001b[0m < \u001b[36m1:08:13\u001b[0m , \u001b[31m82 it/s\u001b[0m ]\n",
      "| rollout/            |          |\n",
      "|    ep_len_mean      | 1.05e+03 |\n",
      "|    ep_rew_mean      | 4        |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    episodes         | 4800     |\n",
      "|    fps              | 170      |\n",
      "|    time_elapsed     | 970      |\n",
      "|    total_timesteps  | 165660   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.00142  |\n",
      "|    n_updates        | 16414    |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=170000, episode_reward=2.90 +/- 3.81━━━━━━━━\u001b[0m \u001b[32m169,997/500,000 \u001b[0m [ \u001b[33m0:17:09\u001b[0m < \u001b[36m1:07:06\u001b[0m , \u001b[31m82 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 846.50 +/- 403.95[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m169,997/500,000 \u001b[0m [ \u001b[33m0:17:09\u001b[0m < \u001b[36m1:07:06\u001b[0m , \u001b[31m82 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m169,997/500,000 \u001b[0m [ \u001b[33m0:17:09\u001b[0m < \u001b[36m1:07:06\u001b[0m , \u001b[31m82 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 846      |\n",
      "|    mean_reward      | 2.9      |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 170000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0015   |\n",
      "|    n_updates        | 17499    |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=180000, episode_reward=10.50 +/- 4.96━━━━━━━\u001b[0m \u001b[32m179,997/500,000 \u001b[0m [ \u001b[33m0:19:28\u001b[0m < \u001b[36m1:06:36\u001b[0m , \u001b[31m80 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 1775.20 +/- 669.87\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m179,997/500,000 \u001b[0m [ \u001b[33m0:19:28\u001b[0m < \u001b[36m1:06:36\u001b[0m , \u001b[31m80 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m179,997/500,000 \u001b[0m [ \u001b[33m0:19:28\u001b[0m < \u001b[36m1:06:36\u001b[0m , \u001b[31m80 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 1.78e+03 |\n",
      "|    mean_reward      | 10.5     |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 180000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.00894  |\n",
      "|    n_updates        | 19999    |\n",
      "----------------------------------\n",
      "\u001b[2KNew best mean reward!━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m179,997/500,000 \u001b[0m [ \u001b[33m0:19:28\u001b[0m < \u001b[36m1:06:36\u001b[0m , \u001b[31m80 it/s\u001b[0m ]\n",
      "\u001b[2KEval num_timesteps=190000, episode_reward=12.30 +/- 3.29━━━━━━━\u001b[0m \u001b[32m190,000/500,000 \u001b[0m [ \u001b[33m0:21:53\u001b[0m < \u001b[36m2:09:58\u001b[0m , \u001b[31m40 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 2020.90 +/- 477.47╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190,000/500,000 \u001b[0m [ \u001b[33m0:21:53\u001b[0m < \u001b[36m2:09:58\u001b[0m , \u001b[31m40 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190,000/500,000 \u001b[0m [ \u001b[33m0:21:53\u001b[0m < \u001b[36m2:09:58\u001b[0m , \u001b[31m40 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 2.02e+03 |\n",
      "|    mean_reward      | 12.3     |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 190000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0126   |\n",
      "|    n_updates        | 22499    |\n",
      "----------------------------------\n",
      "\u001b[2KNew best mean reward!━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190,000/500,000 \u001b[0m [ \u001b[33m0:21:53\u001b[0m < \u001b[36m2:09:58\u001b[0m , \u001b[31m40 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194,105/500,000 \u001b[0m [ \u001b[33m0:22:50\u001b[0m < \u001b[36m1:11:19\u001b[0m , \u001b[31m71 it/s\u001b[0m ]\n",
      "| rollout/            |          |\n",
      "|    ep_len_mean      | 1.47e+03 |\n",
      "|    ep_rew_mean      | 7.66     |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    episodes         | 5200     |\n",
      "|    fps              | 141      |\n",
      "|    time_elapsed     | 1370     |\n",
      "|    total_timesteps  | 194115   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.00634  |\n",
      "|    n_updates        | 23528    |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=200000, episode_reward=17.00 +/- 4.86━━━━━━━\u001b[0m \u001b[32m199,993/500,000 \u001b[0m [ \u001b[33m0:24:18\u001b[0m < \u001b[36m1:00:57\u001b[0m , \u001b[31m82 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 2267.50 +/- 458.34╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199,993/500,000 \u001b[0m [ \u001b[33m0:24:18\u001b[0m < \u001b[36m1:00:57\u001b[0m , \u001b[31m82 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199,993/500,000 \u001b[0m [ \u001b[33m0:24:18\u001b[0m < \u001b[36m1:00:57\u001b[0m , \u001b[31m82 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 2.27e+03 |\n",
      "|    mean_reward      | 17       |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 200000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.00429  |\n",
      "|    n_updates        | 24999    |\n",
      "----------------------------------\n",
      "\u001b[2KNew best mean reward!━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199,993/500,000 \u001b[0m [ \u001b[33m0:24:18\u001b[0m < \u001b[36m1:00:57\u001b[0m , \u001b[31m82 it/s\u001b[0m ]\n",
      "\u001b[2KEval num_timesteps=210000, episode_reward=17.20 +/- 6.95━━━━━━━\u001b[0m \u001b[32m210,000/500,000 \u001b[0m [ \u001b[33m0:26:46\u001b[0m < \u001b[36m1:41:49\u001b[0m , \u001b[31m47 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 2311.30 +/- 859.02m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m \u001b[32m210,000/500,000 \u001b[0m [ \u001b[33m0:26:46\u001b[0m < \u001b[36m1:41:49\u001b[0m , \u001b[31m47 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m \u001b[32m210,000/500,000 \u001b[0m [ \u001b[33m0:26:46\u001b[0m < \u001b[36m1:41:49\u001b[0m , \u001b[31m47 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 2.31e+03 |\n",
      "|    mean_reward      | 17.2     |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 210000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.00459  |\n",
      "|    n_updates        | 27499    |\n",
      "----------------------------------\n",
      "\u001b[2KNew best mean reward!━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m \u001b[32m210,000/500,000 \u001b[0m [ \u001b[33m0:26:46\u001b[0m < \u001b[36m1:41:49\u001b[0m , \u001b[31m47 it/s\u001b[0m ]\n",
      "\u001b[2KEval num_timesteps=220000, episode_reward=14.70 +/- 5.25━━━━━━━\u001b[0m \u001b[32m220,000/500,000 \u001b[0m [ \u001b[33m0:29:20\u001b[0m < \u001b[36m2:29:29\u001b[0m , \u001b[31m31 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 2384.80 +/- 620.910m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m \u001b[32m220,000/500,000 \u001b[0m [ \u001b[33m0:29:20\u001b[0m < \u001b[36m2:29:29\u001b[0m , \u001b[31m31 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------0m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m \u001b[32m220,000/500,000 \u001b[0m [ \u001b[33m0:29:20\u001b[0m < \u001b[36m2:29:29\u001b[0m , \u001b[31m31 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 2.38e+03 |\n",
      "|    mean_reward      | 14.7     |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 220000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0155   |\n",
      "|    n_updates        | 29999    |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=230000, episode_reward=16.40 +/- 6.15━━━━━━━\u001b[0m \u001b[32m230,000/500,000 \u001b[0m [ \u001b[33m0:31:56\u001b[0m < \u001b[36m1:39:05\u001b[0m , \u001b[31m45 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 2149.10 +/- 336.371m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m \u001b[32m230,000/500,000 \u001b[0m [ \u001b[33m0:31:56\u001b[0m < \u001b[36m1:39:05\u001b[0m , \u001b[31m45 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------1m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m \u001b[32m230,000/500,000 \u001b[0m [ \u001b[33m0:31:56\u001b[0m < \u001b[36m1:39:05\u001b[0m , \u001b[31m45 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 2.15e+03 |\n",
      "|    mean_reward      | 16.4     |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 230000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.00324  |\n",
      "|    n_updates        | 32499    |\n",
      "----------------------------------\n",
      "\u001b[2K----------------------------------m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m \u001b[32m233,356/500,000 \u001b[0m [ \u001b[33m0:32:43\u001b[0m < \u001b[36m1:02:46\u001b[0m , \u001b[31m71 it/s\u001b[0m ]\n",
      "| rollout/            |          |\n",
      "|    ep_len_mean      | 2.02e+03 |\n",
      "|    ep_rew_mean      | 12.9     |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    episodes         | 5600     |\n",
      "|    fps              | 118      |\n",
      "|    time_elapsed     | 1963     |\n",
      "|    total_timesteps  | 233362   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0136   |\n",
      "|    n_updates        | 33340    |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=240000, episode_reward=18.50 +/- 5.08━━━━━━━\u001b[0m \u001b[32m240,000/500,000 \u001b[0m [ \u001b[33m0:34:30\u001b[0m < \u001b[36m2:18:07\u001b[0m , \u001b[31m31 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 2548.20 +/- 442.5190m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m \u001b[32m240,000/500,000 \u001b[0m [ \u001b[33m0:34:30\u001b[0m < \u001b[36m2:18:07\u001b[0m , \u001b[31m31 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m \u001b[32m240,000/500,000 \u001b[0m [ \u001b[33m0:34:30\u001b[0m < \u001b[36m2:18:07\u001b[0m , \u001b[31m31 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 2.55e+03 |\n",
      "|    mean_reward      | 18.5     |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 240000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.00624  |\n",
      "|    n_updates        | 34999    |\n",
      "----------------------------------\n",
      "\u001b[2KNew best mean reward!━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m \u001b[32m240,000/500,000 \u001b[0m [ \u001b[33m0:34:30\u001b[0m < \u001b[36m2:18:07\u001b[0m , \u001b[31m31 it/s\u001b[0m ]\n",
      "\u001b[2KEval num_timesteps=250000, episode_reward=19.70 +/- 11.30━━━━━━\u001b[0m \u001b[32m250,000/500,000 \u001b[0m [ \u001b[33m0:37:03\u001b[0m < \u001b[36m1:38:27\u001b[0m , \u001b[31m42 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 2297.20 +/- 798.0191m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m \u001b[32m250,000/500,000 \u001b[0m [ \u001b[33m0:37:03\u001b[0m < \u001b[36m1:38:27\u001b[0m , \u001b[31m42 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m \u001b[32m250,000/500,000 \u001b[0m [ \u001b[33m0:37:03\u001b[0m < \u001b[36m1:38:27\u001b[0m , \u001b[31m42 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 2.3e+03  |\n",
      "|    mean_reward      | 19.7     |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 250000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.00614  |\n",
      "|    n_updates        | 37499    |\n",
      "----------------------------------\n",
      "\u001b[2KNew best mean reward!━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m \u001b[32m250,000/500,000 \u001b[0m [ \u001b[33m0:37:03\u001b[0m < \u001b[36m1:38:27\u001b[0m , \u001b[31m42 it/s\u001b[0m ]\n",
      "\u001b[2KEval num_timesteps=260000, episode_reward=16.30 +/- 6.97━━━━━━━\u001b[0m \u001b[32m259,997/500,000 \u001b[0m [ \u001b[33m0:39:37\u001b[0m < \u001b[36m0:56:17\u001b[0m , \u001b[31m71 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 2120.80 +/- 436.3991m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m \u001b[32m259,997/500,000 \u001b[0m [ \u001b[33m0:39:37\u001b[0m < \u001b[36m0:56:17\u001b[0m , \u001b[31m71 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m \u001b[32m259,997/500,000 \u001b[0m [ \u001b[33m0:39:37\u001b[0m < \u001b[36m0:56:17\u001b[0m , \u001b[31m71 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 2.12e+03 |\n",
      "|    mean_reward      | 16.3     |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 260000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.013    |\n",
      "|    n_updates        | 39999    |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=270000, episode_reward=15.30 +/- 4.12━━━━━━━\u001b[0m \u001b[32m270,000/500,000 \u001b[0m [ \u001b[33m0:42:22\u001b[0m < \u001b[36m1:49:28\u001b[0m , \u001b[31m35 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 2209.10 +/- 571.54[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m \u001b[32m270,000/500,000 \u001b[0m [ \u001b[33m0:42:22\u001b[0m < \u001b[36m1:49:28\u001b[0m , \u001b[31m35 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m \u001b[32m270,000/500,000 \u001b[0m [ \u001b[33m0:42:22\u001b[0m < \u001b[36m1:49:28\u001b[0m , \u001b[31m35 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 2.21e+03 |\n",
      "|    mean_reward      | 15.3     |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 270000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0154   |\n",
      "|    n_updates        | 42499    |\n",
      "----------------------------------\n",
      "\u001b[2K----------------------------------[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m \u001b[32m279,693/500,000 \u001b[0m [ \u001b[33m0:45:10\u001b[0m < \u001b[36m1:02:18\u001b[0m , \u001b[31m59 it/s\u001b[0m ]\n",
      "| rollout/            |          |\n",
      "|    ep_len_mean      | 2.35e+03 |\n",
      "|    ep_rew_mean      | 15.4     |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    episodes         | 6000     |\n",
      "|    fps              | 103      |\n",
      "|    time_elapsed     | 2710     |\n",
      "|    total_timesteps  | 279706   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.013    |\n",
      "|    n_updates        | 44926    |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=280000, episode_reward=15.70 +/- 5.55━━━━━━━\u001b[0m \u001b[32m280,000/500,000 \u001b[0m [ \u001b[33m0:45:35\u001b[0m < \u001b[36m2:02:37\u001b[0m , \u001b[31m30 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 2336.30 +/- 416.81\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m \u001b[32m280,000/500,000 \u001b[0m [ \u001b[33m0:45:35\u001b[0m < \u001b[36m2:02:37\u001b[0m , \u001b[31m30 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m \u001b[32m280,000/500,000 \u001b[0m [ \u001b[33m0:45:35\u001b[0m < \u001b[36m2:02:37\u001b[0m , \u001b[31m30 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 2.34e+03 |\n",
      "|    mean_reward      | 15.7     |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 280000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0121   |\n",
      "|    n_updates        | 44999    |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=290000, episode_reward=22.40 +/- 6.09━━━━━━━\u001b[0m \u001b[32m290,000/500,000 \u001b[0m [ \u001b[33m0:48:34\u001b[0m < \u001b[36m2:12:19\u001b[0m , \u001b[31m26 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 2765.20 +/- 553.72\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m \u001b[32m290,000/500,000 \u001b[0m [ \u001b[33m0:48:34\u001b[0m < \u001b[36m2:12:19\u001b[0m , \u001b[31m26 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m \u001b[32m290,000/500,000 \u001b[0m [ \u001b[33m0:48:34\u001b[0m < \u001b[36m2:12:19\u001b[0m , \u001b[31m26 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 2.77e+03 |\n",
      "|    mean_reward      | 22.4     |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 290000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.00606  |\n",
      "|    n_updates        | 47499    |\n",
      "----------------------------------\n",
      "\u001b[2KNew best mean reward!━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m \u001b[32m290,000/500,000 \u001b[0m [ \u001b[33m0:48:34\u001b[0m < \u001b[36m2:12:19\u001b[0m , \u001b[31m26 it/s\u001b[0m ]\n",
      "\u001b[2KEval num_timesteps=300000, episode_reward=17.60 +/- 4.74━━━━━━━\u001b[0m \u001b[32m300,000/500,000 \u001b[0m [ \u001b[33m0:51:12\u001b[0m < \u001b[36m1:18:03\u001b[0m , \u001b[31m43 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 2519.90 +/- 427.85m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m \u001b[32m300,000/500,000 \u001b[0m [ \u001b[33m0:51:12\u001b[0m < \u001b[36m1:18:03\u001b[0m , \u001b[31m43 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m \u001b[32m300,000/500,000 \u001b[0m [ \u001b[33m0:51:12\u001b[0m < \u001b[36m1:18:03\u001b[0m , \u001b[31m43 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 2.52e+03 |\n",
      "|    mean_reward      | 17.6     |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 300000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0114   |\n",
      "|    n_updates        | 49999    |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=310000, episode_reward=20.30 +/- 7.69━━━━━━━\u001b[0m \u001b[32m310,000/500,000 \u001b[0m [ \u001b[33m0:53:43\u001b[0m < \u001b[36m1:12:57\u001b[0m , \u001b[31m43 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 2769.30 +/- 714.98m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m \u001b[32m310,000/500,000 \u001b[0m [ \u001b[33m0:53:43\u001b[0m < \u001b[36m1:12:57\u001b[0m , \u001b[31m43 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m \u001b[32m310,000/500,000 \u001b[0m [ \u001b[33m0:53:43\u001b[0m < \u001b[36m1:12:57\u001b[0m , \u001b[31m43 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 2.77e+03 |\n",
      "|    mean_reward      | 20.3     |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 310000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.00357  |\n",
      "|    n_updates        | 52499    |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=320000, episode_reward=25.50 +/- 7.55━━━━━━━\u001b[0m \u001b[32m320,000/500,000 \u001b[0m [ \u001b[33m0:56:22\u001b[0m < \u001b[36m1:54:54\u001b[0m , \u001b[31m26 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 3196.50 +/- 763.830m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m \u001b[32m320,000/500,000 \u001b[0m [ \u001b[33m0:56:22\u001b[0m < \u001b[36m1:54:54\u001b[0m , \u001b[31m26 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m \u001b[32m320,000/500,000 \u001b[0m [ \u001b[33m0:56:22\u001b[0m < \u001b[36m1:54:54\u001b[0m , \u001b[31m26 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 3.2e+03  |\n",
      "|    mean_reward      | 25.5     |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 320000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0168   |\n",
      "|    n_updates        | 54999    |\n",
      "----------------------------------\n",
      "\u001b[2KNew best mean reward!━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m \u001b[32m320,000/500,000 \u001b[0m [ \u001b[33m0:56:22\u001b[0m < \u001b[36m1:54:54\u001b[0m , \u001b[31m26 it/s\u001b[0m ]\n",
      "\u001b[2KEval num_timesteps=330000, episode_reward=21.90 +/- 10.10━━━━━━\u001b[0m \u001b[32m329,998/500,000 \u001b[0m [ \u001b[33m0:58:57\u001b[0m < \u001b[36m0:39:58\u001b[0m , \u001b[31m71 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 2679.40 +/- 693.160m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m \u001b[32m329,998/500,000 \u001b[0m [ \u001b[33m0:58:57\u001b[0m < \u001b[36m0:39:58\u001b[0m , \u001b[31m71 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━\u001b[0m \u001b[32m329,998/500,000 \u001b[0m [ \u001b[33m0:58:57\u001b[0m < \u001b[36m0:39:58\u001b[0m , \u001b[31m71 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 2.68e+03 |\n",
      "|    mean_reward      | 21.9     |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 330000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0297   |\n",
      "|    n_updates        | 57499    |\n",
      "----------------------------------\n",
      "\u001b[2K----------------------------------━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━\u001b[0m \u001b[32m333,453/500,000 \u001b[0m [ \u001b[33m0:59:46\u001b[0m < \u001b[36m0:39:24\u001b[0m , \u001b[31m70 it/s\u001b[0m ]\n",
      "| rollout/            |          |\n",
      "|    ep_len_mean      | 2.71e+03 |\n",
      "|    ep_rew_mean      | 20.2     |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    episodes         | 6400     |\n",
      "|    fps              | 92       |\n",
      "|    time_elapsed     | 3586     |\n",
      "|    total_timesteps  | 333459   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0114   |\n",
      "|    n_updates        | 58364    |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=340000, episode_reward=23.20 +/- 5.40━━━━━━━\u001b[0m \u001b[32m340,000/500,000 \u001b[0m [ \u001b[33m1:01:34\u001b[0m < \u001b[36m1:03:48\u001b[0m , \u001b[31m42 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 2767.70 +/- 543.59[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m340,000/500,000 \u001b[0m [ \u001b[33m1:01:34\u001b[0m < \u001b[36m1:03:48\u001b[0m , \u001b[31m42 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m340,000/500,000 \u001b[0m [ \u001b[33m1:01:34\u001b[0m < \u001b[36m1:03:48\u001b[0m , \u001b[31m42 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 2.77e+03 |\n",
      "|    mean_reward      | 23.2     |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 340000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0246   |\n",
      "|    n_updates        | 59999    |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=350000, episode_reward=21.40 +/- 7.06━━━━━━━\u001b[0m \u001b[32m350,000/500,000 \u001b[0m [ \u001b[33m1:04:12\u001b[0m < \u001b[36m1:15:05\u001b[0m , \u001b[31m33 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 2570.30 +/- 745.77[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m350,000/500,000 \u001b[0m [ \u001b[33m1:04:12\u001b[0m < \u001b[36m1:15:05\u001b[0m , \u001b[31m33 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m350,000/500,000 \u001b[0m [ \u001b[33m1:04:12\u001b[0m < \u001b[36m1:15:05\u001b[0m , \u001b[31m33 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 2.57e+03 |\n",
      "|    mean_reward      | 21.4     |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 350000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0402   |\n",
      "|    n_updates        | 62499    |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=360000, episode_reward=25.40 +/- 6.50m━━━━━━\u001b[0m \u001b[32m360,000/500,000 \u001b[0m [ \u001b[33m1:07:02\u001b[0m < \u001b[36m1:34:34\u001b[0m , \u001b[31m25 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 2993.10 +/- 978.76\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m \u001b[32m360,000/500,000 \u001b[0m [ \u001b[33m1:07:02\u001b[0m < \u001b[36m1:34:34\u001b[0m , \u001b[31m25 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━\u001b[0m \u001b[32m360,000/500,000 \u001b[0m [ \u001b[33m1:07:02\u001b[0m < \u001b[36m1:34:34\u001b[0m , \u001b[31m25 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 2.99e+03 |\n",
      "|    mean_reward      | 25.4     |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 360000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0221   |\n",
      "|    n_updates        | 64999    |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=370000, episode_reward=30.30 +/- 14.04━━━━━━\u001b[0m \u001b[32m370,000/500,000 \u001b[0m [ \u001b[33m1:10:12\u001b[0m < \u001b[36m0:58:56\u001b[0m , \u001b[31m37 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 3340.90 +/- 1060.66[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m \u001b[32m370,000/500,000 \u001b[0m [ \u001b[33m1:10:12\u001b[0m < \u001b[36m0:58:56\u001b[0m , \u001b[31m37 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m \u001b[32m370,000/500,000 \u001b[0m [ \u001b[33m1:10:12\u001b[0m < \u001b[36m0:58:56\u001b[0m , \u001b[31m37 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 3.34e+03 |\n",
      "|    mean_reward      | 30.3     |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 370000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0073   |\n",
      "|    n_updates        | 67499    |\n",
      "----------------------------------\n",
      "\u001b[2KNew best mean reward!━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m \u001b[32m370,000/500,000 \u001b[0m [ \u001b[33m1:10:12\u001b[0m < \u001b[36m0:58:56\u001b[0m , \u001b[31m37 it/s\u001b[0m ]\n",
      "\u001b[2KEval num_timesteps=380000, episode_reward=32.40 +/- 11.09m━━━━━\u001b[0m \u001b[32m380,000/500,000 \u001b[0m [ \u001b[33m1:13:22\u001b[0m < \u001b[36m1:07:51\u001b[0m , \u001b[31m29 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 3573.50 +/- 845.59━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m \u001b[32m380,000/500,000 \u001b[0m [ \u001b[33m1:13:22\u001b[0m < \u001b[36m1:07:51\u001b[0m , \u001b[31m29 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m \u001b[32m380,000/500,000 \u001b[0m [ \u001b[33m1:13:22\u001b[0m < \u001b[36m1:07:51\u001b[0m , \u001b[31m29 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 3.57e+03 |\n",
      "|    mean_reward      | 32.4     |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 380000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0137   |\n",
      "|    n_updates        | 69999    |\n",
      "----------------------------------\n",
      "\u001b[2KNew best mean reward!━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━\u001b[0m \u001b[32m380,000/500,000 \u001b[0m [ \u001b[33m1:13:22\u001b[0m < \u001b[36m1:07:51\u001b[0m , \u001b[31m29 it/s\u001b[0m ]\n",
      "\u001b[2KEval num_timesteps=390000, episode_reward=20.20 +/- 9.940m━━━━━\u001b[0m \u001b[32m390,000/500,000 \u001b[0m [ \u001b[33m1:16:35\u001b[0m < \u001b[36m1:02:02\u001b[0m , \u001b[31m30 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 2709.50 +/- 645.99━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━\u001b[0m \u001b[32m390,000/500,000 \u001b[0m [ \u001b[33m1:16:35\u001b[0m < \u001b[36m1:02:02\u001b[0m , \u001b[31m30 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━\u001b[0m \u001b[32m390,000/500,000 \u001b[0m [ \u001b[33m1:16:35\u001b[0m < \u001b[36m1:02:02\u001b[0m , \u001b[31m30 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 2.71e+03 |\n",
      "|    mean_reward      | 20.2     |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 390000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.00634  |\n",
      "|    n_updates        | 72499    |\n",
      "----------------------------------\n",
      "\u001b[2K----------------------------------━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━\u001b[0m \u001b[32m392,433/500,000 \u001b[0m [ \u001b[33m1:17:14\u001b[0m < \u001b[36m0:28:41\u001b[0m , \u001b[31m63 it/s\u001b[0m ]\n",
      "| rollout/            |          |\n",
      "|    ep_len_mean      | 2.95e+03 |\n",
      "|    ep_rew_mean      | 24       |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    episodes         | 6800     |\n",
      "|    fps              | 84       |\n",
      "|    time_elapsed     | 4634     |\n",
      "|    total_timesteps  | 392441   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0134   |\n",
      "|    n_updates        | 73110    |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=400000, episode_reward=34.30 +/- 13.780m━━━━\u001b[0m \u001b[32m400,000/500,000 \u001b[0m [ \u001b[33m1:19:31\u001b[0m < \u001b[36m0:41:45\u001b[0m , \u001b[31m40 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 3421.60 +/- 1190.46━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m \u001b[32m400,000/500,000 \u001b[0m [ \u001b[33m1:19:31\u001b[0m < \u001b[36m0:41:45\u001b[0m , \u001b[31m40 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m \u001b[32m400,000/500,000 \u001b[0m [ \u001b[33m1:19:31\u001b[0m < \u001b[36m0:41:45\u001b[0m , \u001b[31m40 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 3.42e+03 |\n",
      "|    mean_reward      | 34.3     |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 400000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0104   |\n",
      "|    n_updates        | 74999    |\n",
      "----------------------------------\n",
      "\u001b[2KNew best mean reward!━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m \u001b[32m400,000/500,000 \u001b[0m [ \u001b[33m1:19:31\u001b[0m < \u001b[36m0:41:45\u001b[0m , \u001b[31m40 it/s\u001b[0m ]\n",
      "\u001b[2KEval num_timesteps=410000, episode_reward=17.70 +/- 7.4290m━━━━\u001b[0m \u001b[32m410,000/500,000 \u001b[0m [ \u001b[33m1:22:22\u001b[0m < \u001b[36m0:47:14\u001b[0m , \u001b[31m32 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 2465.30 +/- 796.19━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━\u001b[0m \u001b[32m410,000/500,000 \u001b[0m [ \u001b[33m1:22:22\u001b[0m < \u001b[36m0:47:14\u001b[0m , \u001b[31m32 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━\u001b[0m \u001b[32m410,000/500,000 \u001b[0m [ \u001b[33m1:22:22\u001b[0m < \u001b[36m0:47:14\u001b[0m , \u001b[31m32 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 2.47e+03 |\n",
      "|    mean_reward      | 17.7     |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 410000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.012    |\n",
      "|    n_updates        | 77499    |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=420000, episode_reward=39.30 +/- 13.8890m━━━\u001b[0m \u001b[32m420,000/500,000 \u001b[0m [ \u001b[33m1:25:24\u001b[0m < \u001b[36m0:36:44\u001b[0m , \u001b[31m36 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 3992.70 +/- 906.15━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m \u001b[32m420,000/500,000 \u001b[0m [ \u001b[33m1:25:24\u001b[0m < \u001b[36m0:36:44\u001b[0m , \u001b[31m36 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m \u001b[32m420,000/500,000 \u001b[0m [ \u001b[33m1:25:24\u001b[0m < \u001b[36m0:36:44\u001b[0m , \u001b[31m36 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 3.99e+03 |\n",
      "|    mean_reward      | 39.3     |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 420000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.00198  |\n",
      "|    n_updates        | 79999    |\n",
      "----------------------------------\n",
      "\u001b[2KNew best mean reward!━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━\u001b[0m \u001b[32m420,000/500,000 \u001b[0m [ \u001b[33m1:25:24\u001b[0m < \u001b[36m0:36:44\u001b[0m , \u001b[31m36 it/s\u001b[0m ]\n",
      "\u001b[2KEval num_timesteps=430000, episode_reward=28.60 +/- 12.8590m━━━\u001b[0m \u001b[32m430,000/500,000 \u001b[0m [ \u001b[33m1:28:19\u001b[0m < \u001b[36m0:30:49\u001b[0m , \u001b[31m38 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 3607.40 +/- 1033.38━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━\u001b[0m \u001b[32m430,000/500,000 \u001b[0m [ \u001b[33m1:28:19\u001b[0m < \u001b[36m0:30:49\u001b[0m , \u001b[31m38 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━\u001b[0m \u001b[32m430,000/500,000 \u001b[0m [ \u001b[33m1:28:19\u001b[0m < \u001b[36m0:30:49\u001b[0m , \u001b[31m38 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 3.61e+03 |\n",
      "|    mean_reward      | 28.6     |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 430000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.00455  |\n",
      "|    n_updates        | 82499    |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=440000, episode_reward=30.40 +/- 7.28\u001b[90m━━\u001b[0m \u001b[32m440,000/500,000 \u001b[0m [ \u001b[33m1:31:22\u001b[0m < \u001b[36m0:23:06\u001b[0m , \u001b[31m43 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 3351.70 +/- 786.62━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m \u001b[32m440,000/500,000 \u001b[0m [ \u001b[33m1:31:22\u001b[0m < \u001b[36m0:23:06\u001b[0m , \u001b[31m43 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m \u001b[32m440,000/500,000 \u001b[0m [ \u001b[33m1:31:22\u001b[0m < \u001b[36m0:23:06\u001b[0m , \u001b[31m43 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 3.35e+03 |\n",
      "|    mean_reward      | 30.4     |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 440000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0145   |\n",
      "|    n_updates        | 84999    |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=450000, episode_reward=28.30 +/- 15.51[90m━━\u001b[0m \u001b[32m449,997/500,000 \u001b[0m [ \u001b[33m1:34:09\u001b[0m < \u001b[36m0:12:07\u001b[0m , \u001b[31m69 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 2892.00 +/- 890.24━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m \u001b[32m449,997/500,000 \u001b[0m [ \u001b[33m1:34:09\u001b[0m < \u001b[36m0:12:07\u001b[0m , \u001b[31m69 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m \u001b[32m449,997/500,000 \u001b[0m [ \u001b[33m1:34:09\u001b[0m < \u001b[36m0:12:07\u001b[0m , \u001b[31m69 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 2.89e+03 |\n",
      "|    mean_reward      | 28.3     |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 450000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0176   |\n",
      "|    n_updates        | 87499    |\n",
      "----------------------------------\n",
      "\u001b[2K----------------------------------━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━\u001b[0m \u001b[32m459,513/500,000 \u001b[0m [ \u001b[33m1:36:35\u001b[0m < \u001b[36m0:10:06\u001b[0m , \u001b[31m67 it/s\u001b[0m ]\n",
      "| rollout/            |          |\n",
      "|    ep_len_mean      | 3.35e+03 |\n",
      "|    ep_rew_mean      | 31.1     |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    episodes         | 7200     |\n",
      "|    fps              | 79       |\n",
      "|    time_elapsed     | 5795     |\n",
      "|    total_timesteps  | 459521   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0068   |\n",
      "|    n_updates        | 89880    |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=460000, episode_reward=30.00 +/- 10.44\u001b[90m━\u001b[0m \u001b[32m460,000/500,000 \u001b[0m [ \u001b[33m1:37:02\u001b[0m < \u001b[36m0:25:11\u001b[0m , \u001b[31m26 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 3263.30 +/- 713.58━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m \u001b[32m460,000/500,000 \u001b[0m [ \u001b[33m1:37:02\u001b[0m < \u001b[36m0:25:11\u001b[0m , \u001b[31m26 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m \u001b[32m460,000/500,000 \u001b[0m [ \u001b[33m1:37:02\u001b[0m < \u001b[36m0:25:11\u001b[0m , \u001b[31m26 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 3.26e+03 |\n",
      "|    mean_reward      | 30       |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 460000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.00783  |\n",
      "|    n_updates        | 89999    |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=470000, episode_reward=28.10 +/- 7.03m\u001b[90m━\u001b[0m \u001b[32m470,000/500,000 \u001b[0m [ \u001b[33m1:39:57\u001b[0m < \u001b[36m0:13:15\u001b[0m , \u001b[31m38 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 3060.30 +/- 454.57━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━\u001b[0m \u001b[32m470,000/500,000 \u001b[0m [ \u001b[33m1:39:57\u001b[0m < \u001b[36m0:13:15\u001b[0m , \u001b[31m38 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━\u001b[0m \u001b[32m470,000/500,000 \u001b[0m [ \u001b[33m1:39:57\u001b[0m < \u001b[36m0:13:15\u001b[0m , \u001b[31m38 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 3.06e+03 |\n",
      "|    mean_reward      | 28.1     |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 470000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0285   |\n",
      "|    n_updates        | 92499    |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=480000, episode_reward=39.50 +/- 10.68m \u001b[32m480,000/500,000 \u001b[0m [ \u001b[33m1:43:08\u001b[0m < \u001b[36m0:14:04\u001b[0m , \u001b[31m24 it/s\u001b[0m ]t/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 4294.80 +/- 570.78━━━━━━\u001b[0m\u001b[90m╺\u001b[0m \u001b[32m480,000/500,000 \u001b[0m [ \u001b[33m1:43:08\u001b[0m < \u001b[36m0:14:04\u001b[0m , \u001b[31m24 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------━━━━━━\u001b[0m\u001b[90m╺\u001b[0m \u001b[32m480,000/500,000 \u001b[0m [ \u001b[33m1:43:08\u001b[0m < \u001b[36m0:14:04\u001b[0m , \u001b[31m24 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 4.29e+03 |\n",
      "|    mean_reward      | 39.5     |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 480000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.00358  |\n",
      "|    n_updates        | 94999    |\n",
      "----------------------------------\n",
      "\u001b[2KNew best mean reward!━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m \u001b[32m480,000/500,000 \u001b[0m [ \u001b[33m1:43:08\u001b[0m < \u001b[36m0:14:04\u001b[0m , \u001b[31m24 it/s\u001b[0m ]\n",
      "\u001b[2KEval num_timesteps=490000, episode_reward=37.30 +/- 8.300m \u001b[32m490,000/500,000 \u001b[0m [ \u001b[33m1:46:13\u001b[0m < \u001b[36m0:04:17\u001b[0m , \u001b[31m39 it/s\u001b[0m ]t/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 3971.00 +/- 687.38━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m490,000/500,000 \u001b[0m [ \u001b[33m1:46:13\u001b[0m < \u001b[36m0:04:17\u001b[0m , \u001b[31m39 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m490,000/500,000 \u001b[0m [ \u001b[33m1:46:13\u001b[0m < \u001b[36m0:04:17\u001b[0m , \u001b[31m39 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 3.97e+03 |\n",
      "|    mean_reward      | 37.3     |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 490000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0101   |\n",
      "|    n_updates        | 97499    |\n",
      "----------------------------------\n",
      "\u001b[2KEval num_timesteps=500000, episode_reward=40.50 +/- 13.440,000/500,000 \u001b[0m [ \u001b[33m1:49:00\u001b[0m < \u001b[36m0:00:00\u001b[0m , \u001b[31m39 it/s\u001b[0m ]t/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 4049.10 +/- 943.04━━━━━━━\u001b[0m \u001b[32m500,000/500,000 \u001b[0m [ \u001b[33m1:49:00\u001b[0m < \u001b[36m0:00:00\u001b[0m , \u001b[31m39 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------━━━━━━━\u001b[0m \u001b[32m500,000/500,000 \u001b[0m [ \u001b[33m1:49:00\u001b[0m < \u001b[36m0:00:00\u001b[0m , \u001b[31m39 it/s\u001b[0m ]\n",
      "| eval/               |          |\n",
      "|    mean_ep_length   | 4.05e+03 |\n",
      "|    mean_reward      | 40.5     |\n",
      "| rollout/            |          |\n",
      "|    exploration_rate | 0.01     |\n",
      "| time/               |          |\n",
      "|    total_timesteps  | 500000   |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.00544  |\n",
      "|    n_updates        | 99999    |\n",
      "----------------------------------\n",
      "\u001b[2KNew best mean reward!━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m500,000/500,000 \u001b[0m [ \u001b[33m1:49:00\u001b[0m < \u001b[36m0:00:00\u001b[0m , \u001b[31m39 it/s\u001b[0m ]\n",
      "\u001b[2K\u001b[35m 100%\u001b[0m \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m500,000/500,000 \u001b[0m [ \u001b[33m1:49:00\u001b[0m < \u001b[36m0:00:00\u001b[0m , \u001b[31m21 it/s\u001b[0m ]\n",
      "\u001b[?25hSaving to logs/6_c/rlzoo3//dqn/BreakoutNoFrameskip-v4_4\n"
     ]
    }
   ],
   "source": [
    "# CPU version where `n-timesteps` value has been scaled down.\n",
    "# For a good run esp if you have GPU, change it to 10 million i.e. 10000000\n",
    "# on a laptop the CPU version takes about 2 hours. \n",
    "# You can change eval-freq from 10000 to 50000 to speed it up a bit\n",
    "!python -m rl_zoo3.train --algo dqn --env BreakoutNoFrameskip-v4 --n-timesteps 500000 --save-freq 10000 \\\n",
    "--eval-freq 10000 --eval-episodes 10 --log-interval 400 --progress \\\n",
    "--track --wandb-project-name dqn-breakout -f logs/6_c/rlzoo3/\n",
    "\n",
    "\n",
    "# example GPU script\n",
    "\n",
    "# !python -m rl_zoo3.train --algo dqn --env BreakoutNoFrameskip-v4 --n-timesteps 10000000 --save-freq 100000 \\\n",
    "# --eval-freq 25000 --eval-episodes 10 --progress \\\n",
    "# --track --wandb-project-name dqn-breakout-gpu -f logs/6_c/rlzoo3/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GmB-KrcxGyLr",
    "outputId": "761def4b-ae11-44a0-ce25-dcea9d4d815b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-04-14 19:40:02.016160: I tensorflow/core/util/port.cc:111] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2024-04-14 19:40:02.018414: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-04-14 19:40:02.050153: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-04-14 19:40:02.050233: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-04-14 19:40:02.050255: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-04-14 19:40:02.056652: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-04-14 19:40:02.056876: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-04-14 19:40:02.670872: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
      "Loading latest experiment, id=4\n",
      "Loading logs/6_c/rlzoo3/dqn/BreakoutNoFrameskip-v4_4/BreakoutNoFrameskip-v4.zip\n",
      "A.L.E: Arcade Learning Environment (version 0.8.1+53f58b7)\n",
      "[Powered by Stella]\n",
      "Stacking 4 frames\n",
      "Atari Episode Score: 24.00\n",
      "Atari Episode Length 3639\n",
      "Atari Episode Score: 48.00\n",
      "Atari Episode Length 4656\n",
      "Atari Episode Score: 49.00\n",
      "Atari Episode Length 4974\n",
      "Atari Episode Score: 51.00\n",
      "Atari Episode Length 5471\n"
     ]
    }
   ],
   "source": [
    "#### Evaluate Trained Agent\n",
    "\n",
    "!python -m rl_zoo3.enjoy --algo dqn --env BreakoutNoFrameskip-v4 --no-render --n-timesteps 5000 --folder logs/6_c/rlzoo3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "so3K2Qt2GyLr"
   },
   "source": [
    "#### Record a video"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ocVeLpp5GyLr",
    "outputId": "16c77552-2904-4d60-a3ea-28a2b5dd4623"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-04-14 19:40:18.071538: I tensorflow/core/util/port.cc:111] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2024-04-14 19:40:18.073335: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-04-14 19:40:18.102339: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-04-14 19:40:18.102421: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-04-14 19:40:18.102442: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-04-14 19:40:18.108025: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-04-14 19:40:18.108244: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-04-14 19:40:18.697265: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
      "Loading latest experiment, id=4\n",
      "Loading logs/6_c/rlzoo3/dqn/BreakoutNoFrameskip-v4_4/BreakoutNoFrameskip-v4.zip\n",
      "A.L.E: Arcade Learning Environment (version 0.8.1+53f58b7)\n",
      "[Powered by Stella]\n",
      "Stacking 4 frames\n",
      "Loading logs/6_c/rlzoo3/dqn/BreakoutNoFrameskip-v4_4/BreakoutNoFrameskip-v4.zip\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "/home/nsanghi/sandbox/apress/drl-2ed/venv39/lib/python3.9/site-packages/gymnasium/utils/passive_env_checker.py:335: UserWarning: \u001b[33mWARN: No render fps was declared in the environment (env.metadata['render_fps'] is None or not defined), rendering may occur at inconsistent fps.\u001b[0m\n",
      "  logger.warn(\n",
      "Saving video to /home/nsanghi/sandbox/apress/drl-2ed/chapter6/logs/6_c/rlzoo3/dqn/BreakoutNoFrameskip-v4_4/videos/final-model-dqn-BreakoutNoFrameskip-v4-step-0-to-step-5000.mp4\n",
      "Moviepy - Building video /home/nsanghi/sandbox/apress/drl-2ed/chapter6/logs/6_c/rlzoo3/dqn/BreakoutNoFrameskip-v4_4/videos/final-model-dqn-BreakoutNoFrameskip-v4-step-0-to-step-5000.mp4.\n",
      "Moviepy - Writing video /home/nsanghi/sandbox/apress/drl-2ed/chapter6/logs/6_c/rlzoo3/dqn/BreakoutNoFrameskip-v4_4/videos/final-model-dqn-BreakoutNoFrameskip-v4-step-0-to-step-5000.mp4\n",
      "\n",
      "Moviepy - Done !                                                                \n",
      "Moviepy - video ready /home/nsanghi/sandbox/apress/drl-2ed/chapter6/logs/6_c/rlzoo3/dqn/BreakoutNoFrameskip-v4_4/videos/final-model-dqn-BreakoutNoFrameskip-v4-step-0-to-step-5000.mp4\n"
     ]
    }
   ],
   "source": [
    "!python -m rl_zoo3.record_video --algo dqn --env BreakoutNoFrameskip-v4 --exp-id 0 -f logs/6_c/rlzoo3/ -n 5000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 546
    },
    "id": "NaOwr1hdGyLr",
    "outputId": "14b57235-51a2-474b-f9bf-8387c25797b0"
   },
   "outputs": [
    {
     "data": {
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type=\"video/mp4\">\n",
       "        </video>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "video_file_rlzoo = \"logs/6_c/rlzoo3/dqn/BreakoutNoFrameskip-v4_1/videos/final-model-dqn-BreakoutNoFrameskip-v4-step-0-to-step-5000.mp4\"\n",
    "play_video(video_file_rlzoo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m7G2Y0M3VE27"
   },
   "source": [
    "#### Share on Huggingface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 145,
     "referenced_widgets": [
      "9f2ca6397a6f4a398b0e6f34923d3cca",
      "ea60c6b775b7462d861b40713761758e",
      "f31ec00499544ac48ee5310261548f76",
      "d7ace0fd22464db4871f018b12a79611",
      "84358b542bd24609878af191bd83ff97",
      "9d9d28c2d0d848f2aadc9062e030ed82",
      "4e196e169061421f8d52d0bfd697d519",
      "6f8d99b188094e669d224de7d6c094a0",
      "340d4b0bbf1d47f1a75019035013fdbe",
      "22c6cd1a6dce4b8b9ec89fb584596c30",
      "7cb51cb9dfbf43ebb6e075e1112c6c03",
      "2fff8f0c8da244a9951b5fdc1b436906",
      "988dfd5a206d4df18d65a75df6157ca2",
      "aa3fac60ce24462a902bff74037c2725",
      "4cfecf5c28ee451baafb677fd4be194a",
      "c562a12f717a401c9fda65199349f0ad",
      "2f92a0ddb83c4b6d8a81275bb1c4982b",
      "2927baad6a22466488b94575b660f886",
      "e69d1860583e405eaf0f4f9c19657da7",
      "15931a4d626e40e998c78b9b2949492d",
      "804dee270b714a3f95f5225cab8e7967",
      "b1fa39f3cf0f4cb1bf9b04cbac3ea529",
      "0252153d759b44fcb81cf9cd9ca0ab47",
      "4ec09519856f41e1bccab710b400bcad",
      "bfa5867c76eb4918a60a1fbc6206ec6d",
      "431c0dc75bd54266bfb4c32ade028489",
      "f328acf83eb147a29cc059af42e7eaee",
      "7d3b7207e1cb460bbf70cb09bcf51689",
      "46b85b7ab68641f7b5db60b69221a882",
      "af2248ebb64d46cabd0635d5503574fc",
      "df50f0151a8f41a39e0c7be8f7de8ac2",
      "5ea5818d34ae4261aee979c1e4bcd2b1"
     ]
    },
    "id": "MxQPkxRsVK0e",
    "outputId": "34fb5888-42f2-4870-da67-5d0281853baa"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0143c5ec13e045c4a5ccca0a5f8701cc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from huggingface_sb3 import load_from_hub, package_to_hub, push_to_hub\n",
    "from huggingface_hub import notebook_login # To log to our Hugging Face account to be able to upload models to the Hub.\n",
    "\n",
    "notebook_login()\n",
    "!git config --global credential.helper store"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**IMPORTANT**\n",
    "Some users have reported facing following error while running `rl_zoo3.push_to_hub`. \n",
    "\n",
    "```\n",
    "\"Token is required (write-access action) but no token found. You need to provide a token or be logged in to Hugging Face with `huggingface-cli login` or `huggingface_hub.login`. See https://huggingface.co/settings/tokens.\"\n",
    "```\n",
    "\n",
    "In such a case the following command will help you over come the issue\n",
    "\n",
    "```\n",
    "import huggingface_hub\n",
    "\n",
    "huggingface_hub.login(token= <YOUR_HF_TOKEN>,\n",
    "                     write_permission = True,\n",
    "                    add_to_git_credential = True)\n",
    "\t\t\t\t\t\n",
    "```\n",
    "\n",
    "Another alternative is to use following command from command shell where the `venv` or `conda` environment for this repository has been activated and then follow the instructions to set the HuggingFace token.\n",
    "\n",
    "```\n",
    "huggingface-cli login\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1l0EwLXhGyLr",
    "outputId": "31fa035f-5b14-4332-c38d-9da3c6538b5c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-04-14 19:45:16.732447: I tensorflow/core/util/port.cc:111] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2024-04-14 19:45:16.734255: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-04-14 19:45:16.764732: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-04-14 19:45:16.764779: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-04-14 19:45:16.764792: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-04-14 19:45:16.768823: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-04-14 19:45:16.768998: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-04-14 19:45:17.216786: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
      "Loading latest experiment, id=4\n",
      "Loading logs/6_c/rlzoo3/dqn/BreakoutNoFrameskip-v4_4/best_model.zip\n",
      "A.L.E: Arcade Learning Environment (version 0.8.1+53f58b7)\n",
      "[Powered by Stella]\n",
      "Stacking 4 frames\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "Uploading to nsanghi/dqn-atari-breakout-rlzoo-gpu, make sure to have the rights\n",
      "\u001b[38;5;4mℹ This function will save, evaluate, generate a video of your agent,\n",
      "create a model card and push everything to the hub. It might take up to some\n",
      "minutes if video generation is activated. This is a work in progress: if you\n",
      "encounter a bug, please open an issue.\u001b[0m\n",
      "Cloning https://huggingface.co/nsanghi/dqn-atari-breakout-rlzoo-gpu into local empty directory.\n",
      "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/nsanghi/dqn-atari-breakout-rlzoo-gpu into local empty directory.\n",
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      "\u001b[38;5;4mℹ Pushing repo dqn-atari-breakout-rlzoo-gpu to the Hugging Face Hub\u001b[0m\n",
      "Upload file dqn-BreakoutNoFrameskip-v4.zip:   0%|   | 1.00/25.9M [00:00<?, ?B/s]\n",
      "Upload file dqn-BreakoutNoFrameskip-v4/policy.pth:   0%| | 1.00/12.9M [00:00<?, \u001b[A\n",
      "\n",
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      "\n",
      "\n",
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      "\n",
      "Upload file train_eval_metrics.zip: 15.3MB [00:01, 16.0MB/s]                    \u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "Upload file replay.mp4: 15.3MB [00:01, 16.0MB/s]                                \u001b[A\u001b[A\u001b[A\u001b[A\n",
      "Upload file dqn-BreakoutNoFrameskip-v4/policy.pth: 15.3MB [00:09, 1.77MB/s]     \u001b[A\n",
      "\n",
      "\n",
      "Upload file dqn-BreakoutNoFrameskip-v4/policy.optimizer.pth: 15.3MB [00:12, 1.33MB/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Upload file train_eval_metrics.zip: 15.3MB [00:14, 16.0MB/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "Upload file dqn-BreakoutNoFrameskip-v4.zip: 30.5MB [00:21, 1.65MB/s]            \n",
      "\n",
      "\n",
      "Upload file dqn-BreakoutNoFrameskip-v4/policy.optimizer.pth: 15.3MB [00:24, 1.33MB/s]\u001b[A\u001b[A\u001b[A\n",
      "Upload file dqn-BreakoutNoFrameskip-v4/policy.pth: 15.3MB [00:24, 1.77MB/s]\u001b[ATo https://huggingface.co/nsanghi/dqn-atari-breakout-rlzoo-gpu\n",
      "   0470e29..b5d8648  main -> main\n",
      "\n",
      "WARNING:huggingface_hub.repository:To https://huggingface.co/nsanghi/dqn-atari-breakout-rlzoo-gpu\n",
      "   0470e29..b5d8648  main -> main\n",
      "\n",
      "Upload file dqn-BreakoutNoFrameskip-v4.zip: 100%|█| 25.9M/25.9M [00:27<00:00, 1.\n",
      "\n",
      "Upload file dqn-BreakoutNoFrameskip-v4/policy.pth: 100%|█| 12.9M/12.9M [00:27<00\u001b[A\n",
      "\n",
      "\n",
      "Upload file train_eval_metrics.zip: 100%|██| 39.1k/39.1k [00:27<00:00, 1.48kB/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
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      "\n",
      "\n",
      "\n",
      "\n",
      "Upload file replay.mp4: 100%|██████████████| 71.5k/71.5k [00:27<00:00, 2.71kB/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\u001b[38;5;4mℹ Your model is pushed to the hub. You can view your model here:\n",
      "https://huggingface.co/nsanghi/dqn-atari-breakout-rlzoo-gpu\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!python -m rl_zoo3.push_to_hub --algo dqn --env BreakoutNoFrameskip-v4 --exp-id 0 \\\n",
    "--folder logs/6_c/rlzoo3 --n-timesteps 5000 --verbose 1 --load-best  \\\n",
    "--organization nsanghi --repo-name dqn-atari-breakout-rlzoo-gpu -m \"Push to Hub\""
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  },
  {
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   "source": [
    "### Summary\n",
    "\n",
    "In this notebook we saw how to train a DQN agent with experience replay and target networks to play Atari game. If you have access to a GPU, try to train the agent and see how well it plays. We can improve the agent further with many tricks and there are many more papers detailing such attempts. As we go along, we will be implementing many of these variants."
   ]
  }
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